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Optimizing prompts for Opus: cut and reword, don’t add

The thesis of Epic #849. Read this before you touch any text Opus sees.

Every piece of text the model reads is both a cost and a lever: the constitution, the persona, each per-surface prompt, every tool description, every injected context-block label, even the eval judges. The default instinct when a model misbehaves is to add a rule to correct it. For Opus that instinct is almost always wrong. The right move is to find the existing text that is causing the behavior and cut or reword it.

Why “add a rule” fails on Opus specifically

The shared prompt was tuned, fix by fix, against Sonnet’s failure modes (sycophancy, hedging, stiffness, fence-sitting). Opus does not share those failure modes, and it differs in two ways that make accretion actively harmful:

1. Opus follows instructions literally. A reductive verb it’s handed (“Compact”, “Synthesize”, “Drop the noise”) it obeys — it compacts. Sonnet largely ignores those words. So a phrase that is inert on Sonnet is a live instruction on Opus.
2. Opus over-commits. Hand it a maximal instruction (“be exhaustive”, “the complete record”, “breadth so any question has a hit”) and it maximizes — it pads. A counter-rule stacked on top doesn’t cancel the first; now it’s following both, harder.

So when Opus drifts, adding a counter-instruction compounds the problem. It also bloats the prompt, and a longer prompt is itself a cost (tokens, latency, and more surface for cross-rule contradiction).

The method (what to do instead)

1. Find the cause in existing text; remove or reword it. Don’t write a new rule. Ask: what am I already saying that produces this?
2. Show before/after verbatim, every edit. No silent rewrites. The diff is the unit of review.
3. Dry-run on REAL data, not synthetic. Pull production logs/notes (Railway, /debug/query) and run the actual pipeline. A fixture can’t show you what the live distribution does.
4. Measure the axis you actually care about, with the real machinery. For recall that means max(prose_vector, tags_vector) cosine over the real query set — not a vibe read of the text. For length, the production answer_length telemetry. Name the metric before you edit.
5. Ablate: cut vs add vs reword, and measure each. (See #858’s ablation table — cutting the regulars block made the symptom worse; an explicit clause fixed it; the real lever was a layer up.) The first thing you blame is usually not the cause.
6. Length is not the enemy — bloat-without-payoff is. Don’t trim for its own sake. Measure whether the extra text buys the thing (searchability, depth, accuracy). If it doesn’t, it’s pure context cost and should go.
7. Key everything per model; share only the safety floor. A rule earned against Sonnet’s failures mis-fires on Opus. The constitution HARD rules, the memory fence, and protected-path/preflight guarantees are the one universal base and are never loosened per-model. Everything else is per-model.

Worked examples (this epic)

• A rule outlives its cause and starts fighting the surface (#3363, the career ban). Two chart lanes carried “No CAREER or RECORD claims”, earned by a live dry run: given a #1 debut the model wrote “Gracie Abrams’ first chart-topper”, plausible and ungrounded. The rule’s own stated premise was that “chart rows carry position, movement, peak and weeks – never career history – so a superlative about an ARTIST is always invented here”. That premise EXPIRED when #3365 started handing the same lane a grounded career block: the prompt then told the model to make no career claim and, a paragraph later, handed it one to make. Check whether a rule’s premise still holds before you treat the rule as load-bearing – a dead rule does not announce itself, it just quietly works against the surface. The fix was to make the rule CONDITIONAL on the grounding (has_career_block / career_fact), not to delete it: with no fact in hand the ban is still right. And the ban was not even doing its job. Measured live on the projection lane, the flat-ban arm wrote “a solid new entry with no chart history to lean on yet” about Dolly Parton – an act with fifty-one Billboard 200 entries. A ban on career claims does not produce silence about a career; it produces an UNGROUNDED GUESS in the other direction. Supplying the fact is what fixed it: “it’d be her 52nd entry on that chart.”
• A caveat aimed at the model becomes a hedge in the voice (#3363, owner steer). The projection framing opened “It is not a chart position, not a settled number, and not a market price” and went on to lecture that a projection stated as a finished result “is false until Billboard prints”. The owner’s steer was blunt: “don’t give any of those caveats, please, ever.” Saying what the number IS – “tracking for a #4 debut” – keeps the tense honest with no disclaimer attached, so the replacement is one clause (“say what it is TRACKING for, not what it DID”) rather than a paragraph of negations.
• A stated char band becomes the output (#commentary-tone, music drops). The owner called a shipped drop “too ai ish and too long”. The compose prompt named “a tight ~80-100 chars is the sweet spot”; the thirteen most recent drops measured 72-97 chars, dead inside that band, while the prompt’s OWN good examples ran 44-57. The model was obeying the number, not drifting. Two further causes sat in the same prompt: three of its five good examples were TWO sentences, against a rule two blocks above forbidding a second beat, and every example was all-lowercase against the persona’s sentence-case rule. The exemplars beat the rules. Fix: delete the number, make every exemplar one beat, and name the live failure shapes in place of stale ones.
• Then the same trap, in the fix (same epic). The first attempt replaced the 80-100 band with a 40-70 TARGET. Measured before/after (n=11, both models, real compose, graded blind), it made the output WORSE: the model cut the concrete half and kept the verdict, so “that falsetto hook is silky enough to…” became “Ravyn Lenae makes worry sound like a whole vibe”. A tighter number is still a number. What worked was removing the target entirely and stating the stop condition instead: you are done when you have named the part. Length then fell out on its own (84->70 chars on the prod model, 101->92 on Sonnet, over-cap lines 4/11 -> 0/11). Name the condition, not the count.

• Measure the FIXTURE too, not only the change (same epic). The dry-run room blob said “this shift is dragging”, and the after-arm kept tacking on “, perfect for a slow shift”. That read as the fix failing. With a neutral room the same prompt dropped it from 5/5 lines to 2/5, and both survivors added information rather than grading. The cue was in the harness. A before/after where only one shape resists is a reason to check what the fixture is feeding the model.

• A guard that named the words back still handed over the number (chart fall lanes, 2026-09-18). The two desk fall framings forbade every decline verb and then stated both positions (“it sits at #10 this week and was #9 last week, a HIGHER place”) and closed “The reader has both numbers and can see it.” The take did what the sentence said: “sits at No. 10 on this week’s Billboard Global 200, after ranking No. 9 last week” shipped with no decline verb, and the judge scored it 0.85 praising the clause as correct. Owner: “Don’t narrate previous position if it was higher. Just say current position.” The fix cut the number from every model-facing line on a fall (the story clause’s “(was #9)”, the watch blob’s “It was #4 last period.”, the framing’s second position and its closing sentence) and said what to do with the older position instead: leave it out. Then the first dry run found the same shape one field over. With the previous rank gone, 10 of 10 takes wrote the PEAK (“55 weeks deep after peaking at #2”, “off a #2 peak”) – because the story line still handed it over. An earlier, higher position is the steer’s shape whatever the column is called, so the peak left the fall story line too (the run stays: a count, not a position). Re-measured, n=6 on the real row: 0/6, gates 0.88-0.92. A rule that bans a phrasing does not remove a fact the prompt still supplies; the model states what it is handed. The deterministic backstop (has_prior_position) takes the take’s own rank and refuses any other position.
• The same steer, one surface over: the market number (market drop, 2026-09-18). “Miley Cyrus’s ‘Bass Persuades’ first-week forecast has dropped to roughly 61.5K equivalent units.” shipped to X at a 0.9 gate score. Owner: “Never say dropped don’t narrate negative changes just say is at.” The blob said “the market forecasts ~61.5K” and nothing else, so where did “dropped” come from? Two places the prompt itself supplied: the self-dedup block handed the model its own line from three days earlier (“forecast at ~62.4K”), and the SIGNAL rule said “report where it NOW stands, with the direction at most as color”. Thirty days of the surface’s posts showed the leaderboard angle doing the same off the blob’s “down 8 pts 24h” line, with the hint modelling it (“Y cratered 54pts to 34%”): 13 declines narrated. So the cuts, before any checker: the blob no longer carries a downward move at all (an upward one still rides), “with the direction at most as color” became “(‘X is at 34%’); a rise may ride as color”, the hint’s example became “Y is at 34%”, the forecast hint gained “is at ~61.5K units” as its model line. The rule was NOT written as a list of banned verbs (the #1140 lesson: an enumerated list summons what it names); the deterministic backstop (has_market_decline) carries the list instead, where naming the words is the point. Then the dry run on the real blob + history (n=5 per model) showed the reword was not the fix. With the earlier figure still in the history block, Sonnet wrote the decline 3/5 (“is down to ~61.5K”, “down from the ~65.2K mark”) and the guild’s GPT tier 2/5; on the Emmy board GPT went 5/5 -> 0/5 once the blob stopped carrying “down 8 pts 24h”, but Sonnet wrote “down from 62%” off the history line. Every remaining decline was written off a figure the prompt handed over. So the figures left the history block (_withhold_figures), and the leak went to 0/6 on both models – and both models then returned EMPTY 6/6, on both shapes, because the block’s other job was “return EMPTY if the numbers haven’t moved” and it could no longer see the numbers. A block that must carry a fact for one job and must not carry it for another is two blocks: the “did it move” judgment is now its own Haiku call (market_history_news, 15/15 on the live cases) that reads the figures, and the compose reads the lines with every figure withheld and a block that no longer asks it to judge anything. When a cut breaks a second job the same text was doing, split the readers; do not put the fact back.
• Four invitations to a read, and a lane hint that modelled the tail (newsroom market lane, 2026-09-16). The Drake / Polymarket card shipped “…, a pretty cold read this late.” The owner: stop at Polymarket, the tail reads as AI. No rule said to add a read; four pieces of text asked for one – “your read on it” (three times across the compose and its context), a user turn that closed “Drop the read.”, a length rule that allowed “one short clause of context” (an opinion is context), and a lane hint whose example modelled the shape (“what the number means (‘the market’s got X on pace for a #1, calling it about 62% likely’)”). Sonnet copied the example’s opener verbatim on 5/5 runs. Every fix is a cut or a reword; the hint now names the stop condition (“END the line at its anchor”) in place of the example. Then the rewords were not enough on their own, and the ablation found where the rest lived. With the prompt fixed, gpt-5.6-terra went 2/5 to 0/5, but Sonnet still folded a BACKGROUND fact in after the anchor 4/5. The first suspect, the background header’s own “fold that one in as a SINGLE short trailing clause”, was reworded for the lane and moved nothing (4/5). The length rule’s clause allowance took it to 2/5. Blanking EITHER remaining context block (the crowd read, or the background) took it to 0/5 – so the crowd block, which asks for “SENTIMENT + context to sharpen the read”, is withheld on a lane whose line ends at its anchor, and the background stays for fact-checking. Final: 0/5 on all three models, n=5 each. Ablate one block at a time and keep the one that carries facts; the first block you blame is usually not the one that moves the number (the same lesson as #858).

• One example taught a wrong FACT (newsroom certification lane, 2026-09-15). The Jennie RUBY card heroed “1x” over a take that said RIAA Gold. Nothing in the prompt told the classifier to hero a bare multiplier. Its one certification example did: "figure": "7x" for a 7x Platinum wire. The model copied the shape onto every rung, and the rungs that carry no multiplier took a wrong award — “1x” is 1,000,000 units on the RIAA ladder and Gold is 500,000. Measured over the 30 days before the report, 217 of 283 certification classifications answered a bare “Nx”, and 54 of those sat on Gold, Diamond or Silver. The fix added no rule. The example now reads "figure": "7x Platinum", and a second example shows the failing shape (a Gold, "figure": "Gold"). Measured on the real classifier over seven live wires, n=4: 24 of 24 bare multipliers before, 24 of 24 award names after. An example is a fact claim, not only a format. Check each one against the domain, not only against the schema.

• Memory over-compaction (#854/#855). Opus wrote half-length notes. The cause was reductive verbs (“Compact”/”Synthesize”/”Drop”) it followed literally — not a missing “be thorough” rule. Fix: remove the verbs and reframe, no additions. Result: Opus length matched Sonnet (≈6k→13k chars), no rule added. Adding “be exhaustive” first was scar tissue that treated the symptom.
• Recall reframe, then over-correction (#855). Telling the writer the note is searched (“write it to be FOUND, matched by meaning + keyword”) was a good, kept change. But it arrived bundled with maximalism — “the complete record”, “the full searchable record”, “breadth so any question has a hit, depth so the hit is complete”, “the small as much as the big”. The instructive part is what the rigorous dry run found (prod daily #1444, the aggressive vs softened prose, n=5 each, fixed 75-moment checklist): the two versions are a dead wash on every metric — length (13.2k vs 14.1k chars, overlapping ranges), and searchability (recall 0.343 vs 0.344, identical sd). A first n=2 pass had looked like the softened version was ~30% shorter and deeper; n=5 erased that — it was sampling noise. The real lessons: (a) Opus normalizes length regardless of “be exhaustive” wording (it wrote ~13–14k chars either way), so that maximalism wasn’t even buying the length it cost on paper; (b) the maximalism didn’t improve recall at all; (c) trust n≈5 over n=2 — a clean two-sample story is often noise. So cutting the maximalism is justified on prose hygiene (it was thrice-repeated, redundant — “complete record” and “full searchable record” and “the small as much as the big”), not on a measured length/search win. Don’t oversell a metric that isn’t there.
• Ask over-length roast (#858 / #856 — the canonical example, see docs/opus_length_drift.md). Opus ran long on thread-continuation roasts via a defend-then-soften reflex. Two things make this the textbook case:
  • The obvious cut is usually not the lever — ablate to find the real one. The instinct was to blame _OPUS_REGULARS (the don’t-punch-down block) and cut it. The ablation said otherwise: cutting it made the symptom worse (bf_hater 3.0 → 3.3) and wasn’t free (it’s the lane/villain guard). The real driver was a length-sanctioning roast rule a layer up (“a full takedown … is the fun”).
  • Fix the cause’s LAYER; don’t stack a counter-rule in a different layer. The roast sanction lives in Layer 1 (constitution.py CALIBRATION, Sonnet-era, never Opus-optimized); the length rule lives in Layer 3 (_OPUS_LENGTH). Adding a length clause to Layer 3 would just contradict Layer 1 one layer down — two rules fighting. The fix is to reword the Layer 1 line so a roast lands sharp in a line or two and drops the defend-then-soften reflex. (The same investigation surfaced Layer-1↔Layer-3 duplication — STAY IN YOUR LANE in both, DATA INTEGRITY’s 9 bullets vs the lean _OPUS_GROUNDING — i.e. more text to consolidate, not add.)
• Discourse: measure the model on the REBUILT prompt, not the legacy one (#876 / #895, full arc in docs/opus_discourse_eval.md). The first measurement compared Opus on the Sonnet-tuned 43k prompt against Sonnet, found Opus worse (a same-topic head-to-head ~6-2), and nearly concluded “keep Sonnet, don’t build.” That measured Opus on the wrong prompt — the exact confound this epic exists to fix. Given a lean discourse core, Opus flips to the strongest of the three (open-question 1/8 → 8/8, flag-planted 10/16 → 15/16, open-loop Opus 3/4 vs Sonnet 0/4 on the real surface). Four sub-traps it hit, each generalizable:
  • A pairwise “which is better” judge is high-variance — prefer per-item metrics. The same-topic head-to-head swung 6-2 → 4-4 → 2-6 on identical conditions; the stable per-post metrics (quality / open-question / flag-planted) were the trustworthy signal. Distinct from the judge-ERROR lesson below: this is judge VARIANCE — a comparative “A vs B” judge is noisier than an absolute per-item one, so never hang a conclusion on one head-to-head.
  • A reused component carries its HOME surface’s framing. Pulling the ask blocks _OPUS_CITE / _OPUS_TOOLDISC into discourse leaked meta-commentary into a room post — their “the user” / “your answer” 1:1 framing reads wrong in a broadcast post (A/B META-leak 2/8 → 0/8 once swapped for a discourse-framed line). Reuse only the surface-NEUTRAL component (_OPUS_REGULARS); reframe the rest for the surface.
  • Dry-run the REAL wired path, including its fallbacks. An offline A/B (direct _call) looked clean, but the live discourse() path has a forced-search retry (thinking OFF) the offline harness never exercised — it degraded the lean output (~4.0 sentences + selection-narration meta-leak vs the primary’s ~2.3). Skipping that retry on the lean path was the fix. Grade the actual pipeline, not the prompt in isolation.
  • Vivid prompt metaphors get parroted (instruction-echo). “plant YOUR flag first” surfaced verbatim in ~1-2/12 posts (“I’m planting my flag: …”). Reworded to “lead with where YOU land” — same instruction, echo 1/12 → 0/12, keystone metrics held (re-validated because the TASK block is load-bearing). The cause was the prompt’s own memorable noun, not a missing “don’t narrate yourself” rule.
• Two “I am the ground truth” blocks in one prompt (newsroom chart lane, 2026-09-08, #3091). The LIVE CHART block says the settled reading “SETTLES the claim”; the Perplexity BACKGROUND block under it (its header is the search surface’s framing) says its figures “win” and to use them VERBATIM. Handed both, the compose led with the research’s figure 5/5 on the real Choosin’ Texas inputs and the self-gate dropped all
  1. The first fix tried was a counter-instruction on the background (“take nothing from here as a second clause”) – it made it WORSE, 5/5 EMPTY: the model would not reconcile the two week counts and bailed. The fix that worked was structural, the #2774 rule: withhold the research block once a live reading has settled the claim (5/5 ship). A reused block carries its home surface’s authority claim; when two blocks claim it, drop one rather than referee between them.
• Two dead ends on the reply surface’s verbal tics (2026-08-03, after the #1970 payload-list cut). The cut halved the pundit frame (realnoun 28% → 11%), and the owner could still search Discord and see the residue — 21 hits for “flex”, a wall of “the real X” and “nobody’s talking about”. Two more levers were tried on the same rig (Opus, 18 real targets, production context, 2 runs/arm). Both failed, and the failures are the useful part:
  • Showing a surface its OWN recent output makes the tic WORSE. Feeding the last 8 drafts in as “YOU POSTED THESE RECENTLY (vary it — don’t reuse the same opener, verb, or sentence shape)” — the block the wire desks and market drops already carry — took any-tic 9/35 → 19/36 and register flags 1/35 → 6/36. The block is a primer: the phrases you show a model are the phrases it reaches for, and a “don’t reuse” instruction does not cancel that. This is the epic’s core lesson in a new costume, and the trap is that the block is proven elsewhere — the desks use it against topic repeats (did I already post this story), a different axis from phrase repeats. Reusing a component across axes needs its own measurement.
  • A few-shot pool’s sentence frames were a wash. 5 of the 17 CALIBRATION exemplars are the appositive shape (“That ‘See you at the EMMYS’ is the cleanest little jab”), so a k=4 sample carries ~1.2 of them every call — a clean-looking cause. Cutting the block entirely (realnoun 2/36, any-tic 9/36) and sampling only non-appositive lines (5/35, 9/35) both land on baseline (4/36, 7/36). The pool teaches register, not frame; #1957’s “exemplars are a wash” result extends to this axis too. What did work, once the owner pushed back a third time: NAME the phrase. A deterministic detector (utils.x_reply_draft.tic_hits, calibrated on both corpora – it trips 39% of a live week’s drafts vs 6% of the owner’s own posted replies) plus ONE re-ask that hands the model the exact words to drop took tics 9/36 → 2/36 (25% → 6%, p=0.046, register a wash) – i.e. down to the owner’s own rate. The pair is the lesson: “vary it, here’s everything you said” PRIMES the habit; “you wrote ‘the real X’, say it without that” removes it. Same axis, same surface, opposite results, and the difference is specificity. The retry is taken ONLY when it comes back clean, so the guard can never degrade a good line – a fix that can only help is worth shipping at a lower evidence bar than one that trades.

Evaluating model effectiveness (how to measure, not just what to change)

Half of this epic’s lessons were about evaluation method, because a prompt change is only as trustworthy as the measurement that justified it. What we learned:

1. Pick the metric and the axis before you edit. “Better” is meaningless. Name the axis — searchability, length, depth-of-record, fabrication rate, adherence — and define how you’ll measure each before touching text, so you can’t rationalize a change after the fact.
2. Measure with the REAL pipeline, not a proxy. Recall means the production path: max(prose_vector, tags_vector) cosine over the real query set against the real embedding model — because that’s what actually retrieves the note. Eyeballing the prose for “searchability” measures nothing.
3. n=2 is noise; trust n≈5. A two-sample dry run told a clean story (softened was “~30% shorter and deeper”); n=5 erased it (length a wash, depth inconclusive). Model output is high-variance — a clean small-n result is more likely sampling luck than signal. Run enough samples to see the spread, and report the spread (min–max + sd), not just the mean. A mean without a range hides whether the difference is real.
4. Validate the metric itself — a broken metric is worse than none. The n=5 depth judge returned coverage counts above the fixed checklist size (depth

   1.0), i.e. it was nonsense, and a careless read would have “concluded” from it. Sanity-bound every derived metric (covered ≤ total) and discard it loudly when it violates the bound, rather than quoting a number that can’t be real.

5. Isolate the parameter you’re testing. Hold everything else fixed (same source data, same queries, same fixed checklist as the depth denominator) so the only moving part is the one prompt change. A floating denominator (the judge re-deriving the moment count per call) injects variance that swamps the signal.
6. Ablate cut vs add vs reword separately. Don’t test a bundle. #858’s table (baseline / cut-the-block / add-a-clause) is the model: it showed the obvious suspect (cut the regulars block) made it worse and the real lever was elsewhere. You cannot tell which token did the work unless you move one at a time.
7. A wash is a result. If the metric says no difference, say so and decide on other grounds (prose hygiene, owner taste) — don’t manufacture a metric win to justify a change you already made. Honest “no measurable effect” is more useful to the next session than an oversold number.
8. Dry-run on real production data. Pull live notes/logs (/debug/query, Railway EVENT logs); synthetic fixtures don’t carry the live distribution that produces the failure.
9. A judge is itself a model — validate a FAIL before acting on it. The eval judge can be wrong, including failing a correct answer. Worked example (#893): the hardened recap eval failed an Opus recap for saying “SGA won the 2025-26 MVP” — and I first read that as an Opus weakness (“over-asserts an outside fact”). The statement is true; the recap had correctly grounded a real recent fact about the exact topic the room was debating, which is what recap is supposed to do. The judge erred — it couldn’t web-verify a recent result and treated “couldn’t verify” as fabrication, against its own rubric. Two compounding traps: (a) acting on a single judge verdict as ground truth, and (b) it nearly produced scar tissue — a “lean recap core” rebuild for a weakness that does not exist (the #861 wash STANDS). The bar a fabrication judge must enforce is “contradicted by search → FAIL,” never “couldn’t verify → FAIL.” Before you act on a judge FAIL, check the judge was right.

Eval fidelity: model production, or you measure nothing

An eval is only worth its verdict if it feeds the model what production feeds it and exercises the path production runs. Three failure modes this epic closed (#886, the ask/memory eval lift):

1. Feed the production-shaped context, not a barer frame. Every real /ask supplies channel_descriptor (“where am I”) + asker_name (“who’s asking”) + the rich message buffer (reactions / reply-graph / author-tags). The synthetic cases passed none of it, so the model saw a thinner frame than any live call and the verdict measured a different distribution. Fix: feed the same context the cog feeds (cogs/ask.py), verbatim shape.
2. Wire the REAL tools/pipeline — never a reimplementation. eval_ask_tools now builds the real SportsDataHub (ApiSportsProvider + SgoProvider) and calls the real format_scoreboard; the thin handlers just call hub.live_games()/upcoming_games()/recent_finals() exactly as the cog does. A handler that reimplements cog logic in the eval drifts from prod silently — then it grades a fiction. Reuse the production function; the eval’s job is routing + answer quality, not a parallel implementation.
3. Grade the model actually SHIPPED per surface — read it from the DB (cost discipline). Don’t grade every model every run. The scheduled suite reads each surface’s enabled model from the per-guild /menu Models override (settings KV) via resolve_eval_models/merge_enabled: flagless → the single enabled model (the surface default UNION any guild override), so it pays 2x ONLY when a guild genuinely split the surface across models. Wiring detail worth remembering: GitHub’s runners cannot reach Railway’s internal DATABASE_URL host, so the read uses DATABASE_PUBLIC_URL (the public proxy) and is fully fail-open — no URL / unreachable → fall back to the surface default, never block the suite on a DB hiccup.
4. Pin the production model in an ad-hoc dry run too, not only in the suite. Item 3 makes the scheduled suite grade the shipped model. A one-off A/B you run by hand needs the same discipline, and it is easy to skip because the default client is right there. Worked example (#2148): the music desk’s watch lane claimed a chart RE-ENTRY it could not prove, and the fix cut the phrase that offered the claim. The first A/B ran on the surface’s DEFAULT model and scored 0/6 on the BEFORE arm — the bad wording produced no bad output, so the result justified nothing and the edit looked unnecessary. Re-run pinned to claude-opus-4-8, the model that composed the live post, the BEFORE arm scored 3/5 and the AFTER arm 0/5. This is the same Opus-vs-Sonnet split the top of this file describes: a phrase that is inert on one model is a live instruction on another. So read the surface’s enabled model first, pin it, and only then believe the numbers. An unpinned A/B measures a model nobody ships.

Validate the gap is real before you build the fix (proportionality)

The fix is usually smaller than the issue makes it sound — measure the gap before building machinery for it. Worked example (#892): the issue asked for a live-embedding behavioral eval of chunked recall, and I started building it. On inspection the gap had mostly already been closed: the chunk mechanism (the two-stage rank_by_similarity rerank, base64-f32 encode/decode) was already deterministically unit-tested in tests/test_embeddings.py and gating CI, and a live-embedding eval would have been dormant anyway (evals.yml has no OPENAI_API_KEY, so embedding cases self-skip). The one genuinely untested seam was the cog wiring — that cogs/ask.py:_vector_recall passes chunk_rerank_top through. The proportional fix was a ~10-line deterministic wiring test, not a live eval harness. Pattern: enumerate what already covers the risk (unit tests, an existing eval, a dormant-in-CI gate) before adding more — sunk-cost on a half-built heavier fix is not a reason to ship it. (Pairs with the CLAUDE.md “proportional to the problem” rule and the #567 resume-spine → 10-line cog_unload example.)

The standing directive

Apply this to every piece of text Opus sees — constitution, persona, per-surface prompts, tool descriptions, context-block labels, eval judges. Each is a lever to be optimized by cutting and rewording for Opus, not a place to accrete rules. When you’re tempted to add: stop, find the cause, cut it instead.

See also

• docs/opus_length_drift.md (#856/#858) — the worked investigation behind the ask-roast example: the full ablation table, the 4-layer prompt assembly, the cross-layer tension, and verbatim before/after rewords of the Layer-1 roast rule.
• Epic #849 (per-model prompt + eval rebuild) and its milestones: ask #859, memory #860, recap #861, discourse #862, other tiers #863, eval coverage #886.
• The eval-fidelity arc: #886 (coverage lift), #887 (DB-read model axis), #890 (production context + real sports tools), #892 (the proportionality correction), #893 (the judge-calibration correction).

Worked example: the awards write-up (2026-08-30)

The owner asked for two things at once — “cut wording down, it’s too long and repetitive, we repeat the all time context too much”. Both were caused by existing text and existing DATA, not by a missing rule, so both fixes were cuts.

• Length. The weekly intro prompt carried the maximalism this file warns about: “give it the FULL fanfare, the same energy as the big monthly ceremony … make the one crown land big”, plus “Two or three sentences”. Sonnet obeyed the maximalism, not the count — it wrote 3.4 sentences. Cutting the two fanfare clauses and asking for “One or two sentences” landed it at 2.0. The hype prompt also said “2-3 sentences” twice and gave the same instruction twice (“make them feel genuinely SEEN” … “specific enough they’d feel genuinely seen”); cutting the duplicate and halving the count took the pair from 717 to 535 chars (n=5, real guild data, pinned to the shipped model — Sonnet on both surfaces).
• The repeated “all time” context was a DATA bug, not a prompt bug. _winner_receipts fed the winner’s top_post_text — their all-time most-reacted message — into a prompt that says “THIS period”. The model was doing as told with what it was given: 5/5 hype-ups quoted a months-old graduation post as the week’s moment. No rule would have fixed that. Diffing the seed’s highlight reel against the window-start snapshot gives the real this-period post, and the leak went to 0/5. Pattern: when a model keeps reaching for the wrong period, check what period the DATA is from before you write a rule about it.
• Measure the claim, not the vibe. The accuracy axis was named first (“does the post assert a rank the winner did not take”), detected with a regex over the real output, and run on the real board shape that produced it. It caught what a read-through would have excused as hype: “Player of the Week, no debate” and “and it wasn’t close” — 4/5 on a winner who placed 4th.

Worked example: the RIAA board takes + scar removal (2026-09-06)

The owner’s report was a style one – “we’ve been writing like AI lately instead of just reporting plainly” – against one shipped card: “2026 has been kind to Future’s catalog: 59 Platinum certifications so far this year, 9 clear of Kanye West’s 50 among today’s top acts, with SZA’s 27 not far behind.” Every part of that sentence traces to a line in utils/riaa_boards.py:compose_inputs. No rule was added. Four lessons, three of them about the SAME mechanism.

• The take reuses the framing’s vocabulary, so every word in the prompt can ship. The runner-up line read “The runner-ups are giants too”. Measured over the 120 posts @tootsiesbar shipped in the 48 hours to 2026-09-06 16:31Z, three said “giants” back, and the word is in no board’s data. Model-facing text is not only instruction; it is also a word list.
• The first rewrite was WORSE, and only a second dry run caught it. Cutting the adjective looked like the whole job. The replacement – “Name one or two runner-ups with the tally the block gives each, not only the gap behind the leader” – did remove the tells and quietly tripled a different failure. On the Platinum-singles board, whose block states exactly ONE gap (the leader against second), the take began working out a SECOND one: “35 clear of Future (102) and 53 ahead of Lil Wayne (84)”, where 53 is the model subtracting two rows. ungrounded_numbers drops that before the self-gate, so the cost is a spent compose. Invented-number rate, n=8 per wording on the same board: the original 2/8, the first rewrite 6/8.
• The obvious cause was the wrong one – ablate, do not reason. The hypothesis was the word “gap”: the instruction said it, so the take wrote gaps. A wording with no “gap” in it at all (“Name one or two runner-ups by the number the block prints beside each”) came back 5/8 – no better. The real work in the old line was done by “the runner-ups are GIANTS TOO”, which makes each runner-up its OWN subject. Without that, “name them” is satisfied most cheaply by extending the comparison already in the sentence, so the model writes a second gap. The wording that keeps the frame and drops the adjective – “Name one or two runner-ups. Each stands on its own number from the block, so state that number and nothing more” – lands at 1/8, better than the original, with 0/8 tells. Same lesson as #858: the thing you are about to cut is usually not the lever.
• Deleting the line scored best and was still the wrong call. A fourth arm removed the runner-up line outright. It is the cleanest on every failure axis – 0/10 invented numbers, 10/10 through the self-gate on the fastest board against 8/10 with the line, and 0/10 of the identity claims in #3019 against a 1/10 baseline. It also stops doing the line’s job: all ten year-board takes named the leader and second and nobody else, so a ten-row board came out as a duel. “Cut it” is the default this file argues for, and the test of a cut is whether the text was buying anything. This one was.
• Use the echo deliberately when a downstream guard bans a word. The certified-units opening said the board “adds” the two formats. The take answered “combined”, which output_checks.has_row_arithmetic reads as a sum of the drawn rows and drops before the self-gate (live 2026-09-05 17:06:34Z, one compose spent; the 18:03Z retry shipped only because it happened to write “spanning”). Rewording the verb to “spans” did NOT fix it: n=5, 5 of 5 still said “combined”. Putting the word SPANNING in the instruction did: 5 of 5 said “spanning”, 0 of 5 said “combined”. Seeding the word you want beats removing the word you don’t, and it beats banning it.
• An opening a subject cannot carry becomes a personification. The year board was told to “Open on the YEAR and the run of new plaques it has produced”. A year produces nothing, so the only way to follow that instruction is to give the year a personality. That is where “2026 has been kind to Future’s catalog” came from. The instruction now names the count.
• A scope instruction outlives the scope it describes. The same block ordered the take to keep “among today’s top acts”. That was true while the board ranked a 40-name roster and false since #2906. All five before-takes carried the false fence; none of the five after-takes does. When a surface’s population changes, grep the prompt for the old population – the fence lives in the model-facing text as well as in the code.

Method note: the axis was named before the edit (does the take carry an AI tell, and does it carry a fence the board does not have), it was counted over the REAL shipped corpus first, and the before/after ran on the real boards through the real wired compose path (compose_market_drop with the desk’s own _CONTEXT), scored by the real self-gate.

And one axis was NOT named before the edit, which is why the first rewrite shipped a regression into a PR. The n=5 pass measured the tells and the false fence – the things the owner reported – and nothing else. The invented gap only appeared when the second pass widened to every board and counted every guard. A prompt edit can move an axis you were not watching, so measure the guards the surface already runs, not only the axis you set out to fix.

Count the failure directly, not through the guard that happens to catch it. The invented gap was found through ungrounded_numbers, which flags a figure the source never states. That guard undercounts this failure: on the 2026 board the take wrote “28 ahead of Rod Wave (31)”, a computed gap whose value happens to equal another act’s row count, so it reads grounded and ships. The honest metric was a direct one – any “N clear of / ahead of” the block does not itself state.

The scar in the same file

The owner asked for the scar to come out in the same pass. Four kinds were there, and naming the kinds matters more than the four cuts, because each recurs:

• A rule with two definitions. Four boards ended their own scope with a row-arithmetic ban and the shared tail banned it again in the very next sentence. One definition now, in the tail.
• Our changelog, inside the prompt. The Diamond scope carried “That was NOT true while the board ranked the streaming roster (#2919), and it is true now (#2915).” A model cannot act on our PR history. It was the only issue number in any model-facing string in the file.
• A proof that was another board’s data. The same sentence proved “a catalogue act IS on this board” with “Garth Brooks leads the album board with 10” – on the SINGLES framing. _number_grounding reads the framing, so a singles take quoting 10 would have passed the guard. The claim survives; the proof is gone.
• Engineering detail the take can never state. The year scope explained our credit parser with two examples of a credit we cannot split (“‘Of Mice & Men’ is one band, ‘Parmalee & Blanco Brown’ is two”). The residual is still stated; the worked examples are in the code, where the next reader needs them.

The head also said one thing twice, twice: “do NOT list the rows” beside “do not recite the leaderboard”, and the card’s credit stated both before and after the rule that depends on it.

Worked example: ablating the RIAA framing line by line (2026-09-06)

Owner steer: “deleting prompt lines is the best strategy, inspect more and ship”. So every line of the RIAA board framing was deleted in turn and measured against its own baseline – five candidates, three boards, n=10 per arm, real model, real wired path, real self-gate.

The steer is right about ONE line and wrong about the other four, and the shape of the result is the useful part.

deletion	platinum invented gap	year identity claim	acts named
(baseline)	1/10	0/10	3.0 / 2.6
the runner-up line	0/10	0/10	1.1 / 2.0
“Never invent a figure”	4/10	3/10	3.0
the credit-tag ban	5/10	1/10	3.0
“in a line or two, then stop”	7/10	1/10	2.9
the re-COUNT clause	4/10	1/10	3.0

Four unrelated cuts all degrade the SAME board, so the lever is not any one line. A shorter framing leaves the take room to invent an angle, and the angles it reaches for are the ones the guards exist to catch: a gap the block never stated, and an identity claim. Cutting “Never invent a figure” is the clearest case – it is one short sentence, it looks like boilerplate next to a longer grounding rule that already says the same thing, and removing it quadrupled invented numbers and took identity claims from 0/10 to 3/10.

Only the runner-up line was free of that, and only because deleting it removes the comparison itself. No runner-up in the take means no gap to work out. It is the one deletion shipped, and its cost is stated rather than hidden: act coverage falls from 3.0 names to 1.1 on the Platinum board. The card still draws every row, so the board is not lost – the take just stops narrating it.

One cut was board-dependent, which is its own warning. Deleting “in a line or two, then stop” is the best result on the YEAR board (123 chars against 142, one sentence, no new failures) and the worst on the Platinum board (7/10 invented). A cut measured on one board and shipped everywhere would have looked like a clean win. It appears in seven files across the repo; none of them were touched.

Method note. output_checks.count_sentences reads a decimal point as a sentence end, so any take carrying “336.5M” measures as four sentences. Every length reading here uses characters instead. A metric that is wrong in the same direction on every arm still ranks the arms correctly, but it cannot be reported as a sentence count, and the production answer_length telemetry has the same artifact.

Worked example: the age of the record (2026-09-06)

Symptom. Two X posts off kworb chart rows closed with an age: “pulling 14.2M streams a track over a decade old” and “a catalog cut over four decades old still finding new spikes”. Axiom showed 20 shipped watch-chart takes in 30 days with the same tail (“thirteen years deep”, “sixteen years after its release”, “twenty years on”). The owner asked to cut them.

Cause, found in existing text. The blobs for those lanes carry a position, a move and a play count, and no age. The shared _CONTEXT block (cogs/music_desk.py) carries an AGE / STAGE paragraph written for the Luminate tracking head, whose line really does say “catalog, out 4.2 years”. It opened as a fact about every post:

AGE / STAGE: the head names the release’s milestone – ‘debut week’, ‘first month’, … Put that milestone in your own words, fresh each post … a catalog album’s weekly number is a this-week read (‘46M this week, eleven years deep’) …

Twenty-odd lanes hand the model that paragraph beside a row with no age in it. The model did what it was told and supplied the age from its own knowledge. The _drop_ungrounded digit check already dropped “14 years old”, which is why the shipped tails are all in words.

Fix: reword the cause into a condition, no counter-rule stacked elsewhere.

AGE / STAGE: a Luminate head names the release’s milestone – … WHEN the head states one, put that milestone in your own words … When the material states NO age or milestone – a chart position, a rank move, a play count – say nothing about how old the title is: a chart row carries no release date, so its age is not in the material.

Measured (real model, n=6 per arm, the exact production blob + framing). Bruno Mars “Locked out of Heaven” #50 -> #33 on the Global Weekly: before 3/6 takes carried an age (“pushing 14 years old”, “years past its debut”, “aging like wine”), after 2/6 (“14 years deep”, “the 2012 cut”). The Billie Jean Spotify-jump blob went 0/6 -> 0/6 on this model. So the reword lowers the rate and does not zero it, and n=6 cannot separate 2/6 from 3/6. A prompt rule the model breaks intermittently gets a deterministic backstop (output_checks.ungrounded_age_claim, reason=ungrounded_age), the same call as has_career_claim and the #2081 digit check. Measured against 30 days of shipped takes (n=5774): the pattern matches 67; the lanes whose blob states an age (tracking, lookup_board) stand down, and the rest are true fabrications.

Lesson. A paragraph that is true for ONE lane’s material reads as an instruction on every lane that shares the block. When a shared block describes the material (“the head names X”), write it as a condition on the material, not a fact about it.

Worked example: the called-shot benchmark fence (2026-09-07)

#3014 handed the called-shot compose ONE new fact — what the artist’s own last album opened at. The existing fence justified itself with a sentence that the new fact made false:

before: ... a projection row carries a rank, a unit count, a sales split and a label, and nothing else. Anything more would be you guessing.

Per the method, the stale clause was reworded, not counter-ruled — no second rule stacked on top saying “but you may also use the benchmark”. The first attempt then over-corrected, and the live dry run is what caught it:

attempt 1: ... Two points are not a trend: say what the gap IS, never that it is a rise, a fall, a record or a best-since

Three live composes on the real building chart, all gating 0.92, and one of them wrote “up big from the 18k his last album opened with” — a direct violation that the judge passed without blinking. The rule was wrong, not the model. Handing it two numbers and forbidding it to say which is larger is incoherent: comparing is saying which is bigger, and the comparison is the entire reason the figure was added.

after: ... and the ONE prior opening above. That figure is this artist's OWN last album, and comparing the two numbers is allowed -- it is the reason you have it. What you have is TWO POINTS, not a career: never 'his biggest ever', never 'his best since', and never a claim about where his career is heading

Five further live composes, all 0.92, all landing on arithmetic over the two given numbers (“more than 5x his last album’s 18K opening”) with no career or trajectory claim.

Two lessons, both already in this doc and both re-earned the hard way:

1. Ban the thing you actually mean. “Never say it rose” was a proxy for “never claim a career trajectory”, and the proxy caught the legitimate case. Opus follows literally, so an over-broad ban is either obeyed (losing the feature) or ignored (as here) — and an ignored rule is still prompt bloat.
2. A passing self-gate is not a passing fence. The judge scored the violating post 0.92 because the post was accurate and well-formed. Only reading the output against the rule showed the rule and the output disagreeing.

Worked example: the gap with nobody in it (2026-09-09)

The projected-units board (chart_boards.projected_top_sales) shipped this to X on 2026-09-08, self-gate 0.88:

Projected Billboard 200 units for Sep. 19 span ~69K to ~34.8K, with HITS Daily Double forecasting a ~4K gap at the top.

Ella Langley’s “Dandelion” was the #1 at ~69K and the post never named it (owner: “this should’ve narrated dandelion”). The cause was in the existing text, twice over. The board’s headline (THE FACT the model reads) opened with the span and named the leader second, and its steer said:

before: Then lead with the SPREAD rather than who is #1: the ranking board already said that, and a units order IS the chart order, so without its own angle this is the same post twice. The fact here is the DISTANCE between these numbers

That clause was written in #2098 to stop four projection boards opening with the same leader. It over-reached: “rather than who is #1” reads, to a literal model, as “do not name who is #1”, and the take obeyed. The reword keeps the angle and restores the subject (_PROJ_STEERS["proj_top_sales"]):

after: Then NAME THE ALBUM ON TOP and its units figure: the leader is the subject of this take, and a gap with nobody named in it is not a post. YOUR ANGLE IS THE DISTANCE -- how far clear of #2 that album is, or how tightly the rest of the board is packed. The ranking board owns positions and moves; you own the leader's units and the gaps.

The headline now opens with the leader too ("Dandelion" by Ella Langley is tracking for ~69K units, the most on the projected Billboard 200 for Sep 19, per HITS, ~4K clear of the #2 album. The top 10 runs from ~69K down to ~34.8K.). The class sweep found the same clause on the platform play-count steer (_PLATFORM_STEERS["platform_streams"]) and reworded it the same way.

Live dry run on the same HITS document (scripts/dryrun_chart_boards.py --compose):

Hits Daily Double has Ella Langley’s “Dandelion” leading the projected Billboard 200 for Sep 19 at ~69K units, only about 4K clear of Olivia Rodrigo’s ~65K, tightest race atop the chart in a minute. – gate 0.88, SHIP

The rank board’s take from the same run named Dandelion too, and the two passed the wording-only dedup the chart cards ride (SequenceMatcher 0.43 against a 0.6 threshold; 0.49 against the rank take that actually shipped on 2026-09-08). The lesson is the “ban the thing you actually mean” one again: the thing meant was “do not build the take around the leader’s rank”, and the proxy “rather than who is #1” banned the subject.

Worked example: a clause the prompt demanded and the lane could not supply (2026-09-14)

Owner steer, on three shipped @tootsiesbar posts: “cut second sentence catalogue talk”.

The takes:

Billie Eilish cleared 8 billion streams on the year, a catalog run that refuses to let go. Kanye West cleared 8.3 billion streams on the year, a massive catalog-and-current run. Tyler, The Creator cleared 3.2 billion streams on the year, another massive catalog-and-current run.

The lane is the music desk’s ANNUAL year-to-date milestone (cogs/music_desk.py, _CONTEXT MID-YEAR MILESTONE + the streams-milestone note). Four of four annual takes that night ended in a clause that restates the figure in words. The self-gate scored them 0.90 and called the filler “one clause of real context”, so nothing downstream was going to catch it.

The cause was a mandate, not a missing ban. The paragraph read '<artist> cleared <X> streams on the year' + ONE short trailing clause, then STOP, and it named three sources for that clause: the momentum trend, the year-end projection, the rank. All three are withheld on this lane – momentum is gated to the non-annual streams facts, the projection is banned by the streams-milestone note in the same context, and a rank only arrives when a chart or qualifier block lands. So on most slots the model was told to write a clause and handed no fact to write it from. It wrote one anyway.

The paragraph already carried the counter-rule. “never a closing flourish” sat two clauses after the mandate and lost to it – the canonical shape this file warns about: a ban stacked beside the instruction that causes the behaviour does not cancel it.

The fix is one reword: the clause is CONDITIONAL on a fact. The format line now stops at the figure, and a trailing clause is allowed only to carry a hard fact a context block states. Given none, the post ends at the figure. The streams-milestone note appended for a measured report lost its own half of the sanction (“one sharp human beat about how big it is”), but only on the annual lane (streams_milestone_note(annual=...)). A NON-annual measured report heads its blob with a grounded release stage – “debut week”, “first month, 3 weeks in”, “catalog, out 4.2 years” – and the shared AGE / STAGE paragraph asks the take to put that milestone in its own words. The stage sits in the BLOB, not in a context block, so the annual wording would have stripped the stage framing off every weekly report whose fail-open supplemental blocks came back empty. The first version of this PR missed that and the Codex reviewer caught it. The lesson generalizes past this fix: a rule keyed to “a fact from a context block” silently excludes the reading’s own head, and a shared block is appended on more lanes than the one you are looking at – so check which lanes it reaches before you narrow what it allows. The annual head carries “year-to-date” and no age at all, so it loses nothing. Dry-run both halves: the non-annual takes still read “four years deep and barely slowing down” / “six weeks in and still holding a real number”.

Measured on the real wired path (compose_market_drop with the desk’s own _CONTEXT and the real blob; Sonnet, the lane’s default; the three reported artists, n=5 each, run with and without a Billboard-standing block):

arm	takes	ending in an invented clause
before	18	18
after	30	0

The chart fact still rides when the block names that artist (“cleared 8.3 billion streams on the year, and ‘Graduation’ is still charting at No. 148 on the Billboard 200 this week”), and the two artists the block did NOT name correctly ignored it.

Two guards were measured, not assumed, because a shorter take could have gone dark two ways. The self-gate scores the bare figure 0.90-0.92 against the real blob (the same score the flourish got), so nothing new is dropped. The arbitrated dedup still ships four same-shape annual takes in a row: the mechanical gate matches the shape in both arms, and the judge overturns it on the artist and the number. A first dedup run said the opposite and was wrong – the harness called the judge with the wrong signature, so every judge call raised and the fail-closed direction left the mechanical verdict standing. Check that your harness’s judge actually answers before you read its verdict.

A shared block is read by more surfaces than the one you are looking at (2026-09-14)

The second Codex finding on #3252, and the more general of the two. The clause rule was written as “a HARD FACT a context block below states”. That phrasing assumes the surface HAS context blocks. cogs/music_desk.py:_CONTEXT is shared with cogs/music_alert.py, whose _fire passes it with no supplemental blocks at all – so on the alert path the rule named a source that does not exist, and the only material the take had (the market blob, carrying the line’s own move) fell outside what the rule allowed. Fixed by keying the rule to the MATERIAL rather than to a block that may not be there.

The predicted symptom did not reproduce, and saying so matters more than the fix. The review expected the alert to discard the move and post the already-known year-to-date count. Measured on the real classifier + the real compose (an annual line_move, Sonnet, n=8 per arm), the figure rode in 8/8 of every arm, the flagged wording included – the model used the blob’s pace line regardless of what the rule said it could use. The fix is right because the wording was wrong, not because it rescued a post.

What the measurement DID find is a weakness that predates the PR. An annual line_move alert fires because the market-implied number moved, and the take almost never says so:

arm	names the MOVE	names the figure
main (pre-PR control)	0/8	8/8
the flagged wording	0/8	8/8
the fix	2/8	8/8

main scoring 0/8 is the whole point of running a control: without it this reads as a regression the PR caused, and it is not one. The cause is upstream of any clause rule – the MID-YEAR FORMAT makes the year-to-date figure the mandatory lead and demotes the move to a trailing clause, on a lane whose entire reason for firing is the move. That is a separate change on a separate lane, so it was FILED, not folded into this PR.

The scar in the same block (2026-09-14)

Owner steer in the same session: “cut scars”. Two of the four kinds this file names were in cogs/music_desk.py:_CONTEXT, and both are the FIRST kind – a rule with two definitions.

• The LENGTH rule had THREE definitions across two layers, and the numbers disagreed. The system prompt owns it for every desk surface: claude_client._POST_LENGTH (“ONE sharp sentence, ~20 words, then stop … Pick the SINGLE sharpest beat and let it carry”), plus the market-drop tail (“never the figure AND a projection AND a trend AND a flourish; if the figure alone lands, stop AT the figure”). _CONTEXT then restated both – “HARD LENGTH LIMIT (the #1 rule): ONE sentence, ~25 words” and “Stacking the figure AND a projection AND a trend AND a flourish is a banned paragraph”. The second clause is near-verbatim the system tail; the word counts are simply different numbers for the same rule. Cut to the one thing this block alone knows: the several context blocks are OPTIONS for the single clause, not a checklist.
• The MID-YEAR paragraph closed on its own opening rule, plus a stale fence. “Use the line’s figure as given – not the full every-digit count, and not a rounded-off version.” The paragraph opens 60 words earlier with exactly that (“the figure EXACTLY as the line gives it … both digits kept, never rounded off”). The “every-digit count” half fences a shape the model is never handed: format_reading has put humanize_units(value_so_far) – “8B” – in the blob since the owner’s “no one needs the exact count” steer. A fence for material that no longer exists is the “scope instruction outlives its scope” case again.

Ablated on the real wired path before cutting (compose_market_drop, the desk’s own _CONTEXT + the real blob, Sonnet, n=5) across three cases, so a cut that fixes one axis could not quietly break another:

case	axis	baseline	scar cut
annual, no chart block	ends at the figure	5/5	5/5
annual, chart block names the artist	quotes the chart fact	5/5	5/5
weekly catalog album	states the release stage	5/5	5/5

Mean length moved 9.0 → 9.0, 20.8 → 22.4 and 22.6 → 19.8 words: noise in both directions at n=5, no trend. So this cut is justified on prose hygiene alone, not on a measured win – the same honesty the #855 entry insists on. 287 characters out of a 3917-character block, and one contradiction the model could have resolved either way is gone.

Two candidates were examined and NOT cut, because each still earns its place: the AGE / STAGE paragraph’s closing “a chart row carries no release date, so its age is not in the material” (it reads like engineering detail, but it is the reason the rule holds, and that lane’s whole failure mode was the model supplying an age the row never stated), and the streams-milestone note’s “no exact digit-string” (it fences a FABRICATION the model can produce unprompted, not an echo of the material, so the stale-fence argument does not apply).

Worked example: the weekly streams beat (2026-09-29)

Owner steer on weekly streams posts that ended “a steady start”: “any commentary should come from the stats, like the qualifiers. If it’s just an empty human beat, remove it.”

The #3252 fix above made the clause CONDITIONAL on a fact, but only on the annual lane. The weekly lane kept three sources of an empty beat, and all three were cut, not countered:

• streams_milestone_note said “Then one sharp human beat about how big it is.” It now has one rule for every measured report: a clause carries a hard fact the material states, else the figure is the whole post.
• The RELATIVE READ block handed the model a Songstats momentum verdict. Its STEADY branch said “a normal week (say so plainly)”, so the model said so. The block, the momentum fetch, and lookup_artist_momentum are gone.
• The blob head carried the release stage (“debut week”, “catalog, out 4.2 years”), and the shared AGE / STAGE paragraph asks the take to put a stated stage in its own words. A measured report now builds its blob with include_stage=False. Market reads keep the stage, because the paragraph is shared with them.

A rank from the background block and a verified qualifier still ride as the one clause, because they are stats.

Worked example: the credit moves to the card (2026-09-29)

Owner steer, on an X post that ended “HITS Daily Double has it there.”: “drop them from the text post and make them on the card. The pill is enough.”

The cause was a mandate. attribution.credit_rule said “CREDIT HITS, AND DO IT IN THE SENTENCE”, and gave in-voice examples that named the source (“hits has her tracking ~74k”). A ban on the press-release tag sat beside it. The model obeyed the mandate and moved the name around the sentence. The rule now says only that the card names the data source, so the line names none.

A name in the instruction is a name in the take. The first rewrite said “THE CARD NAMES HITS DAILY DOUBLE AS THE SOURCE”. On the reconciliation lane, Sonnet still wrote “HITS Daily Double projected #3” on 3 of 8 takes. Three more places carried the name to the model: called_shot.describe (the take’s topic line), the block’s per-publisher labels, and the rule itself. Each was reworded by ROLE (“the industry projected #3”), and the rule stopped naming any source. The next run was 0 of 10.

Measured on the real compose inputs of six lanes (compose_market_drop, the desk’s own _CONTEXT + each lane’s framing):

arm	model	takes naming a source
before (main)	terra	23/30
after	terra	0/48
after	Sonnet	1/48

The one Sonnet miss was on the reconciliation lane. The widened has_attribution_tag drops it after one _detag_credit re-ask, which now runs on every desk lane.

Platform boards are the exception, and it is deliberate. On a Spotify or Apple Music board the platform IS the chart (“#1 on spotify’s us chart”), so naming it is the fact, not a credit. Those boards keep their own chart phrasing (platform_chart_phrasing).