Use when you have a hunch, a candidate optimization, a novel technique, or an "I think X is faster/better/possible" idea for tensor-grep and must turn it into an ACCEPTED result or a DOCUMENTED RETIREMENT — the research discipline. Covers the evidence bar (one mechanism must explain every observation INCLUDING the negatives, and survive an assigned adversarial refutation whose every claim cites file:line), predicting the number + noise band BEFORE you run, the idea lifecycle (experimental default-OFF -> dogfood/benchmark -> council-verify -> conscious flag-flip to adopted OR retirement recorded in docs/PAPER.md so it is never retried), verifying an AI-drafted plan against real code, and where good ideas actually come from. The scientific-method META sibling to change-control (gates) and research-frontier (targets).
Instalación
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Use when you have a hunch, a candidate optimization, a novel technique, or an "I think X is faster/better/possible" idea for tensor-grep and must turn it into an ACCEPTED result or a DOCUMENTED RETIREMENT — the research discipline. Covers the evidence bar (one mechanism must explain every observation INCLUDING the negatives, and survive an assigned adversarial refutation whose every claim cites file:line), predicting the number + noise band BEFORE you run, the idea lifecycle (experimental default-OFF -> dogfood/benchmark -> council-verify -> conscious flag-flip to adopted OR retirement recorded in docs/PAPER.md so it is never retried), verifying an AI-drafted plan against real code, and where good ideas actually come from. The scientific-method META sibling to change-control (gates) and research-frontier (targets).
tensor-grep research methodology
This is the how-do-we-know-it-is-true runbook — the scientific method for tensor-grep. It answers a
different question than its siblings: not "is this change allowed?" (that is tensor-grep-change-control)
and not "what should we work on?" (that is tensor-grep-research-frontier), but "what has to be true
before a hunch is allowed to be called a result — and what do we do with the hunch when it loses?"
tensor-grep is described in its own docs as a benchmark-governed, contract-heavy codebase where you
do not optimize by guesswork (AGENTS.md:15). The whole product wedge is trustworthy context for an
agent — "the product wedge is not 'faster grep'" (AGENTS.md:177) — so a result that looks right but
was never actually proven is not a small sin here; it is the exact failure the product exists to prevent.
This skill is the discipline that keeps hunches honest.
Who this is for
Two readers at once — write and act to the lower bound of each:
A Sonnet-class AI in a cheap autonomous session: you need the hard gates and copy-pasteable checks so
you cannot rationalize noise into a win or ship your first-guess mechanism.
A mid-level human engineer with zero repo context: you need the why — the theory of evidence — so
you apply the bar to a new idea you have never seen before.
When to use this skill vs a sibling
Your situation
Use
You have an idea/hunch/candidate and must prove or retire it (this whole flow)
this skill
"Is this change allowed to land? which GATE applies?" (registration, fail-closed, push-race, TDD)
tensor-grep-change-control
"What are the worthwhile TARGETS / open research questions to pursue?"
tensor-grep-research-frontier
"Has this idea already been TRIED and lost?" (settled battles)
tensor-grep-failure-archaeology
Actually running a benchmark / reading check_regression.py / launcher attribution
tensor-grep-benchmark-and-proof-toolkit
A specific live bug / red CI / hang to diagnose
tensor-grep-debugging-playbook
The eval/oracle/grader version (a whole lane fails, a grader passes an empty answer)
trustworthy-cuj-scoring
The internals/contracts an experiment must respect (front door, fail-closed backend)
tensor-grep-architecture-contract
How a semantic-search experiment specifically is scoped/run
tensor-grep-semantic-search-campaign
This skill does not route around change-control. It tells you when a result is believable; it never
tells you a change is shippable. A believable result still passes every gate in tensor-grep-change-control
(TDD-first, dogfood the real binary, one-merge-per-tick, autonomy stops at a draft PR). If this skill and
change-control ever seem to conflict, change-control wins — stop and reconcile.
Part 1 — The evidence bar (what makes a result "accepted")
A result is accepted in this repo only when it clears all three tests below. Miss any one and what you
have is a candidate, not a result. Label it that way in the PR, the doc, and your own head.
Test A — one mechanism must explain EVERY observation, including the negatives
State the single mechanism you believe is causing the effect ("Aho-Corasick single-pass beats N sequential
scans", "IDF weighting is missing so common terms rank as high as rare ones"). Then list all the
observations it must account for — and deliberately include the ones that would embarrass it:
The negative controls. A no-match is a real outcome your mechanism must survive, not an error to hide.
GPU/search benchmarks treat rg exit code 1 with empty output as a valid comparator outcome when
tg also returns zero matches (AGENTS.md:173) — a mechanism that only "works" on matching inputs and
silently mis-handles the empty case has not been proven, it has been cherry-picked.
The edge/adversarial inputs. CRLF, invalid UTF-8, BOM, binary/NUL-byte files, multiline. rg's
default engine matches invalid UTF-8 but PCRE2 requires valid UTF-8 and transcodes — so a mechanism
that swaps engines changes results, not just speed (AGENTS.md Backend Fail-Closed Contract, :233-244).
The disconfirming measurement. If your mechanism predicts a win and the fair measurement shows a
loss, the mechanism is wrong (or incomplete) — you do not get to keep the mechanism and blame the ruler.
Worked failure — the IDF ranking flip (a mechanism that did NOT explain everything). A change that
added zero ranking terms silently flipped the agent capsule from "tie, ask the user" to "confidently pick
a marker no-op." The first-guess mechanism — "IDF weighting is missing" — was disproven by a thinktank,
and the minimal "just widen the candidate pool" fix was proven insufficient (tensor-grep-failure-archaeology
Battle 7). The real mechanism was a stack: a flat no-IDF scorer + a hard top-5 candidate cap + a
file_score flip + an alphabetical path tie-break. Lesson: your first single-cause story usually fails
to explain one of the observations. Keep pulling until one mechanism covers them all — and note that the
repo's rule for that surface is to harden the tie/marker detection to be robust, not relax the failing
test, because relaxing masks the real degradation (AGENTS.md:185).
Test B — the hypothesis must PREDICT THE NUMBER (and the noise band) before you run
Before you run the benchmark or the eval, write down the number you expect and the direction — e.g.
"this should cut cold tg search from ~0.26s to ~0.20s, and the run-to-run jitter here is ~5ms so anything
under a ~20ms delta is noise." Committing to the prediction first is what stops the two classic
self-deceptions: (1) rationalizing scheduler jitter into a "win" after the fact, and (2) moving the goalpost
to whatever the run happened to produce.
This is not optional ceremony; it is forced by how measurement works here. Sub-10ms timings are dominated by
process-spawn and OS-scheduler jitter, not by your code — the benchmark harness enforces a min-baseline-time-s
floor and an absolute-jitter tolerance for exactly this reason (mechanics + constants in
tensor-grep-benchmark-and-proof-toolkit). If your predicted effect is smaller than the noise band, the
experiment cannot detect it no matter what it prints — redesign it (bigger corpus, more repetitions, a
regime where the effect is larger) before spending a run. For the eval/pass-rate version of this rule —
pre-state the SNR / noise floor before claiming any fix "improved" a score — load trustworthy-cuj-scoring
and noise-floor-before-quantitative-claims.
The house rule this enforces (tensor-grep-change-control Part 1, rule 3): no speed/improvement claim
without a measured number vs the accepted baseline. Predicting the number first just means you decided
what would count as success before you were tempted by the result.
Test C — the result must survive an ASSIGNED adversarial refutation, every claim citing file:line
A result you graded yourself is a hypothesis. The bar is that it survives a distinct, adversarial pass
whose job is to break it — and in this repo that pass has a hard evidentiary rule:
A finding or claim with no file:line citation is DISCARDED (AGENTS.md:231).
Two named passes exist, and they are different stages, not one:
Pre-build planning review / council — before you implement, an independent review cites file:line
for every seam claim in the plan; uncited claims are hypotheses, not facts (AGENTS.md:227,:271). A
citation-enforced review of this kind caught 5 blockers in two unverified plans in a single session.
Post-build adversarial audit — a mandatory, separately named stage that adversarially reviews the
integrated diff, re-audit -> fix-wave -> re-audit until ZERO must-fix findings remain; that
zero-finding state is the convergence gate before a draft PR (AGENTS.md:231,:295). This stage once
caught a HIGH CUDA-fork hazard that 203 passing tests missed — which is the whole point: green tests
are not the adversary; a hostile reader with citations is.
When the codex/gemini council CLIs are unavailable, run the council as a Workflow of Claude lenses with
the same citation enforcement (use-thinktank fallback). The mechanism you claim must survive someone
actively trying to show it is wrong or off-strategy, not just survive not being examined.
Worked failure — the fair-baseline refutation. "Aho-Corasick single-pass beats N sequential scans" is
true against the wrong comparator (N separate rg process spawns) and false against the right one: rg
has its own batched primitive, rg -F -e pat1 -e pat2 ..., and against that the batched tg route was
~2.3x slower (tensor-grep-benchmark-and-proof-toolkit worked example; AGENTS.md:174 names the fair
baseline). The mechanism did not survive an adversary who insisted on the comparator's own batched form. The
repo kept the (correct) code but marked the row diagnostic, not release-gating — see Part 5's retirement
discipline.
Part 2 — Verify an AI-drafted plan against the real code (before you build)
Most ideas now arrive as an AI/subagent-drafted plan. Treat every factual claim in that plan as a
hypothesis until it cites a file:line that actually resolves (AGENTS.md:223-231). AI plans have a
consistent failure mode: plausible-sounding edit locations that do not match the real structure — dead code
paths, renamed symbols, already-fixed lines. Reading the real files before you implement is not overhead; it
is the gate that prevents wasted cycles.
Two research-specific traps, both hard-won here:
Never trust a self-report. A subagent's "tests pass" / "N green" is a hypothesis until external state
confirms it — an exit code, a real-binary dogfood, or a citation that resolves. Re-run any validation a
subagent claims to have passed; worktree fan-out branches have no .venv, so their "tests pass" is
literally un-runnable in their own tree (AGENTS.md:229; tensor-grep-change-control Part 1).
Green ≠ working when the test never touches the real boundary. Mock-based FFI tests were green while
the real PyO3 bridge was dead and dropped every forwarded flag (tensor-grep-failure-archaeology
Battle 8). Prove an FFI/bridge mechanism with a live call into the built extension (maturin develop,
then confirm the flag actually reached rg), and prove generated/detached code by executing it
(compile() + exec() the string and assert the behavior, e.g. the checksum gate fires beforeos.replace), not by reading substrings (AGENTS.md:229).
The step-by-step of this pass and its citation rules live in the global skill verify-plan-against-code and
in tensor-grep-change-control Part 6 — this skill only tells you why it is part of the evidence bar: an
unverified plan is an unfalsified hypothesis wearing a to-do list.
Part 3 — The idea lifecycle (default-OFF -> proven -> adopted, OR retired-in-writing)
Every non-trivial idea rides one track. It ends in exactly one of two terminal states — adopted or
retired — and both are written down. An idea that is neither adopted nor retired is a landmine: the
next agent re-discovers it and re-loses the same day.
Stage
What happens
Gate to advance
Owner skill for the mechanics
1. Hypothesis
State the mechanism (Test A) + the predicted number & noise band (Test B).
Plan verified against real code (Part 2).
this skill + verify-plan-against-code
2. Experimental / default-OFF
Build behind a flag or an opt-in path; ships default-OFF. GPU, LSP, semantic, provider-classify all live here.
Behavior-change starts with a failing test; the flag defaults off.
tensor-grep-change-control (Part 1, rule 4)
3. Dogfood / benchmark
Prove it on the real published binary and/or the right benchmark vs the accepted baseline.
Passes the measured bar you predicted; artifact carries launcher mode/kind; no stale in-tree binary.
Pre-build council + post-build adversarial audit (Test C), re-audit until zero must-fix findings.
Zero uncited/unresolved must-fix findings.
this skill (Test C) + use-thinktank
5a. Adopted (conscious flag-flip)
A human flips the default on — never an agent, never auto-merge. Endpoint of any autonomous fan-out is a draft PR.
The flip is a deliberate act after 3+4, not a side effect of a merge.
tensor-grep-change-control (Part 1, rule 1)
5b. Retired (written down)
The idea lost (regressed, or the gain was not stable enough to justify merge). Record the attempt in docs/PAPER.md so no future agent retries it.
The dead end is in the ledger with the number that killed it.
this skill (Part 5)
Experimental-until-proven is a hard rule, not a preference: GPU, LSP, semantic-search, and
provider-classify (cybert) stay default-OFF and labeled experimental until correctness AND speed AND UX
are all proven — never market an unproven wedge (tensor-grep-change-control Part 1, rule 4; GPU is
currently slower than CPU and its P1 CUDA kernel is paused, AGENTS.md:245-253). "Experimental" is a
lifecycle stage with an exit gate, not a permanent excuse.
The instrumented-build-gate fork (C12, added 2026-07-08) — for a speculative idea with appeal but no demand proof
Some ideas fail Test B differently than a mis-measured speed claim: the mechanism is plausible, the
build is cheap enough, but there is no evidence anyone actually needs it — a hunch about future
value, not a validated user ask (contrast with "local hybrid semantic search," which is a validated
#1 ask, AGENTS.md:249). Forcing a straight build-vs-drop choice on that kind of idea is a false
binary. Load the global skill instrumented-build-gate (folds into this stage of the lifecycle,
does not replace it) for the discipline: (1) three options, not two — build-now / do-nothing /
document-the-already-shipped-adjacent-value-and-instrument-to-measure; (2) capture any already-shipped
adjacent value as a near-$0 docs-only PR immediately, regardless of which fork you take; (3) if you
instrument, the probe must be minimal and safe — fail-open (a metrics/telemetry seam must never break
serving), PII-free (hash, never store raw user text), bounded (day-bucketed, LRU-capped, single-writer
atomic write), and behind a kill-switch, and it should extend an existing diagnostic surface (doctor
/ status) rather than register a brand-new command/flag (sidesteps the 4-registration-site hazard
entirely); (4) write the time-boxed numeric threshold BEFORE the measurement window opens, and do
not renegotiate it after seeing partial data — that is Test B's "predict the number first" rule applied
to a demand signal instead of a speed number; (5) a failed gate is still a win: it is a
document-only decision earned for near-$0, and it is written down like any other retirement (Part 5).
Worked example (Problem 4d of tensor-grep-research-frontier, 2026-07-08). A proposal to build a
local multi-agent "ledger" on top of tg's session/daemon plane hit exactly this fork: the mechanism
was plausible (a repo-scoped session store + a warm loopback daemon already let one agent reuse
another's work) but there was no measured evidence of real concurrent-agent demand on this repo. The
three-way fork resolved it as DOCUMENT-NOW-BUILD-LATER-ON-GATE: ship a docs-only positioning page
(docs/multi_agent_context_plane.md) for free, plus a small opt-out demand-instrumentation patch on
the session daemon (concurrent-distinct-client counter + repeat-expensive-artifact counter, both
fail-open/PII-free/hashed, read back via existing tg session daemon status / tg doctor surfaces)
that earns or fails a pre-stated 2-week numeric gate before any ledger/claims layer is authorized. As
of this writing that build (#456) is open, not merged to main — cite it as an in-flight design
pattern, not a shipped result (git log --oneline origin/main | head to re-check).
Worked example — a fresh default-OFF -> proven -> pending-flip lifecycle (TG_FIND_DENSE_WEIGHT, #189/#628/#630, 2026-07-16)
A clean, in-progress instance of stages 2-3 (the "adopted" stage 5a has NOT fired yet — do not describe
this knob as flipped). tg find's golden-set sweep (benchmarks/eval_late_rerank_quality.py) measured
a real ndcg@10/recall@10 lift from a 1:5 bm25:dense fusion weight, with zero per-category regression, on
the 40-query NL golden set — that is Test B's "predict the number, then measure" working as designed.
But the sweep is 100% NL queries and cannot see the opposite failure mode (a short/lexical query where
BM25 is the stronger leg and boosting dense regresses it) — so the knob shipped stage 2, experimental
default-OFF (TG_FIND_DENSE_WEIGHT unset = 1.0 = today's byte-identical equal-weight fusion), scoped
by a query-shape classifier so a non-default env value only ever applies where the sweep actually
measured the lift (multi-word queries), never to a literal/identifier lookup. The classifier itself
then failed its OWN Test A on the first real-corpus dogfood: the original morpheme-count gate
(split_terms(query) > 2) mis-boosted 5 of 6 literal-identifier golden queries because a descriptive
single-token identifier (reciprocal_rank_fusion, _confine_mcp_path) splits into 3+ morphemes — fixed
by switching to a whitespace word-count gate (#191, commit 173e093/#630), which also added a
math.isfinite clamp so TG_FIND_DENSE_WEIGHT=nan/inf degrades to the safe default instead of
poisoning reciprocal_rank_fusion's sort. Stage 3 (dogfood/benchmark) is now satisfied; stage 5a
(conscious flag-flip to a non-1.0 default) is a separate, still-open CEO checkpoint — evidence in hand
does not itself authorize the flip. See tensor-grep-semantic-search-campaign STATUS UPDATE 2 and
tensor-grep-config-and-flags for the mechanics.
Retirement is a deliverable — the ledger
docs/PAPER.md is the repo's research notebook, and §3.10 "Optimization Ledger: Accepted Wins and
Rejected Dead Ends" exists for one reason it states outright: "To avoid re-running the same failed ideas."
When a candidate loses, you are not done until it is in that ledger. Real entries already there — treat them
as the format to copy:
PyO3 for directory walking — expected an "astronomical speedup"; measured 48.818s (Rust PyO3
ignore ext) vs 39.892s (pure Python os.walk); the FFI boundary (per-path GIL serialization) is the
bottleneck, not the walk. Reverted; do not re-propose (docs/PAPER.md §3.7; tensor-grep-failure-archaeology
Battle 1).
Onefile Nuitka binary as a speed path — built tg.exe ~1.10–1.22s vs Python bootstrap ~0.33–0.48s;
onefile extraction overhead dominates. "Not the current path to parity with raw rg" (docs/PAPER.md
§3.10, and the note at §3.10's "Current honest state").
Native positional in-place search --replace — retired because it violated the stable search contract
by mutating files from a search-style flag; file edits stay on run --rewrite ... --apply (docs/PAPER.md
§3.8). A correctness/strategy retirement, not a speed one.
A whole list of "correct but slower" posting/decode/cache micro-optimizations that "looked
mathematically plausible but lost empirically" (docs/PAPER.md §3.10 "Important rejected candidates").
The discipline is blunt (tensor-grep-change-control Part 1, rule 3): if a candidate is correct but
slower, revert it and record the attempt in docs/PAPER.md. Do not ship a clean regression because "the
code is nicer," and do not leave the dead end unwritten.
Part 4 — Where good ideas actually come from (so you can go get more)
Good candidates here have not come from armchair invention; they have come from three repeatable sources.
When you need a new idea (that is the tensor-grep-research-frontier job), mine these — and when you
evaluate an incoming idea, weight it by which source it came from.
Source
What it is
Repo receipts
How to mine it
Dogfood
Using the real tg binary on real work surfaces the highest-signal gaps.
tg registration-check is on the roadmap as a "real-use-validated agent-native differentiator" (AGENTS.md:250); the scripts/dogfood/ harness has repeatedly caught contract bugs CliRunner could not see.
Run the shipped binary on a real task (dogfood-the-shipped-artifact); log every friction point.
Competitive analysis
Reading what peers/tools do and stealing the idea (not the code) with correct licensing.
The #1 roadmap item cites a concrete reference architecture — MinishLab Semble (tree-sitter chunking + potion-code-16M Model2Vec + BM25 + RRF, CPU-only, MIT) (AGENTS.md:249); docs/PAPER.md benchmarks tg against Aider/Cody/Cursor-class peers and Gemini/Copilot.
Structured web research (use-exa); produce a "steal-list" of ideas with license notes; ideas are free, code import needs the upstream notice.
Audits
A tiered adversarial read of already-committed code finds bugs no one re-verified.
The Security Hardening (Round-3) patterns are literally an audit lens — sweep targets to check proactively because the bugs lived in committed code where no one re-checked (AGENTS.md:255-262).
Run codebase-audit / omega-deep-dive-bughunt; every finding cites file:line or is discarded.
Worked example (C14) — a token-economy benchmark, not a speed benchmark, surfaced a moat gap.
The Dogfood row above is usually read as "run the binary and look for friction"; the 2026-07-08
receipt shows a benchmark can be the dogfood instrument too, just measuring a different axis than
wall-clock. The first oracle-validated tokens-per-correct-answer run (Sverklo bench:primitives,
express@4.21.1, 25 tasks) found tg7.5x better than grep on definition-lookup — the expected
moat win — but roughly an order of magnitude worse on file-dependency questions ("what does file
X import"), because tg has no scoped file-dependency primitive and an agent pays a whole-repo
tg map to answer a single-file question. This is Test A working as designed: the mechanism ("the
context moat wins on token economy") predicted a win and a real measurement produced a disconfirming
result on one task class — which is not a refutation of the moat thesis, it is a scoped gap inside
it (tensor-grep-research-frontier Problem 4b's C14 sub-item, #74). Re-verify the exact multiplier
before citing it: the committed-artifact number and the informal receipt in memory
(tensor-grep-benchmark-proofpoint-2026-07-08) had not yet been reconciled as of this writing — see
docs/benchmarks.md and tensor-grep-benchmark-and-proof-toolkit before quoting a precise figure.
C14, continued (2026-07-16) — the full lifecycle, not just the disconfirming measurement. This is
worth tracking through to its end because it is a rare in-repo example of every Part 3 stage actually
firing in sequence: (1) hypothesis — the context moat wins on token economy; (2) disconfirming
measurement — P4 file-deps landed ~10x WORSE than rg (53,631 tokens-per-correct via whole-repo
tg map), not a win, exposing a genuine scoped gap (no file-dependency primitive existed); (3) fix
— tg imports FILE / tg importers FILE [ROOT] shipped (#460, 05f49b8, the normal 4-site
registration plus MCP tools); (4) re-verify — the SAME Sverklo P4 task slice re-run independently
(deterministic, $0) landed at 2,387 tokens-per-correct, ~2.24x BETTER than rg, F1 improved
0.542->0.606, bidirectional-oracle-validated 25/25 (v1.76.12, #619); (5) adopted — the primitive
is shipped and load-bearing; the only remaining gate is publishing the numbers, which is a separate,
CEO-held decision (#72), not a research-methodology gate. Test A's "one mechanism must explain every
observation, including the negatives" held throughout: the SAME context-moat mechanism correctly
predicted both the P1 win and, once the scoped-gap fix landed, the P4 win — the disconfirming
measurement was not a refutation, it was the diagnostic that pointed at the missing primitive.
Two guardrails on idea selection (the frontier owns the full target list; this is just the methodology):
Weight ideas by the strategy, not by novelty. Raw search speed is the parity tier; the moat is the
agent-native context layer (orient / callers / blast-radius / the token-efficient capsule)
(AGENTS.md:177,:245-253). A clever idea that only makes cold grep marginally faster is off-strategy even
if it works.
A validated user ask outranks a speculative one. "Local hybrid semantic search" is the top roadmap
item because it is the #1 validated user ask and the biggest competitive gap (AGENTS.md:249), not
because it is the most novel.
Pre-acceptance checklist (run before you call a hunch a "result")
One mechanism stated, and it explains every observation — including the negative controls
(no-match), the edge inputs (CRLF/UTF-8/BOM/binary/multiline), and any disconfirming measurement.
The number and noise band were predicted BEFORE the run; the predicted effect is bigger than the
noise floor (else the experiment cannot detect it — redesign).
Measured against the accepted baseline with the right benchmark, on the real binary (no
stale in-tree binary, launcher command_kind == native_exe or explicitly disclosed) — mechanics in
tensor-grep-benchmark-and-proof-toolkit.
For any batched/amortized claim, compared against the comparator's own batched primitive
(rg -F -e ...), not a sequential-loop strawman.
Survived a post-build adversarial audit: re-audit -> fix-wave -> re-audit to zero must-fix
findings, every surviving claim citing a file:line that resolves (uncited = discarded).
The plan behind it was verified against real code; no subagent self-report was trusted un-re-run;
any FFI/bridge proven with a live extension call, any generated code proven by executing it.
Terminal state chosen and written down: adopted via a conscious human flag-flip (endpoint =
draft PR), OR retired with the killing number recorded in docs/PAPER.md so it is never retried.
Nothing here skipped a gate in tensor-grep-change-control (TDD-first, dogfood, one-merge-per-tick,
no auto/admin-merge).
If you cannot tick a box, you have a candidate, not a result. Say "candidate" out loud.
Provenance and maintenance
Volatile facts were originally dated 2026-07-02, release v1.17.25; AGENTS.md citations
re-grepped and re-anchored 2026-07-08 against v1.49.3; the C14 lifecycle extension and the
TG_FIND_DENSE_WEIGHT worked example were added and verified 2026-07-16 against v1.78.1 (AGENTS.md
has continued to gain sections since 2026-07-08 that shift downstream line numbers — do not trust a
stale number, re-grep it; the table below carries the numbers verified at v1.49.3, not yet re-walked
for v1.78.1). Re-verify before relying on any of them — a wrong methodology runbook lets a bad result
through, which is worse than none.
global skill instrumented-build-gate; worked example memory tensor-grep-a2a-ledger-audit-2026-07-08
If any command above no longer matches, update this skill in the same change — and check whether the sibling
that owns the fact (change-control, benchmark-toolkit, failure-archaeology, research-frontier) needs the
same update. Do not trust the numbers stamped here on a future session without re-running the greps —
AGENTS.md line numbers have already drifted once this way (a new section inserted upstream silently
invalidated every downstream citation in this file simultaneously).
