skills/skills-codex/meta-apply/SKILL.md
Privileged applier that LANDS meta-optimize / corpus-audit patches the user approved — the ONLY skill permitted to mutate the skill corpus from a self-modification proposal, with cross-model jury and human approval at landing. Use when the user says "meta apply", "/meta-apply", "land the staged patches", "应用优化", after a /meta-optimize run.
npx skillsauth add wanshuiyin/Auto-claude-code-research-in-sleep meta-applyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill exists to enforce a privilege boundary. Producers like
/meta-optimize (and, later, corpus-audit) are
read-only — no Write/Edit, no apply step; they can only stage candidate patches
under .aris/meta/pending/. This skill is the only place a staged patch becomes a real
change to the corpus. Splitting "propose" from "land" across two skills with different
tool grants is what makes "a loop cannot apply its own patch" structural rather than a
sentence the producer is asked to obey.
It is human-invoked only. It runs when the user explicitly types /meta-apply after
reading the producer's REPORT. That invocation IS the landing authorization (the locked
2026-05-30 decision: the human stays at the landing gate).
The earlier design trusted a jury_verdict: pass field in the producer's manifest. That
is forgeable — the producer writes that field, so a buggy/misaligned producer could
stage a killed patch labelled "pass." So this skill does not trust any producer-written
verdict. It runs the cross-model jury itself, at landing time, on the actual staged
diff (fresh reviewer, paths-only). The verdict is produced where it is consumed, by the
privileged human-invoked skill — nothing to forge.
For each staged patch the user asks to land, in order — any failure ⇒ skip & report, never silently apply:
/meta-apply 1,3
or all); default to applying nothing.spawn_agent
(a different model family from the executor running this skill; reasoning_effort: xhigh, read-only, paths-only per
reviewer-independence.md) on the
staged .diff + its target. Ask: does this change improve the harness without
regressions; PASS or KILL + one-line reason. KILL ⇒ refuse. The human cannot
override a KILL — they may only pick among jury-PASSED survivors. (A loop can DRIVE;
only the cross-model jury can ACQUIT.)provenance.py assert_cross_family — if it raises (same family / unknown), refuse. The check is the
structural backstop that keeps the jury genuinely cross-model.PENDING=".aris/meta/pending"
[ -d "$PENDING" ] || { echo "Nothing staged. Run /meta-optimize first."; exit 0; }
echo "Staged:"; cat "$PENDING/manifest.jsonl"
Resolve provenance.py via the 3-layer chain in
integration-contract.md §2
(.aris/tools/ → tools/ → $ARIS_REPO/tools/).
For every patch the user asked to land, read its staged .diff and target, then spawn the
fresh reviewer jury (Rule 2) — paths-only, no producer reasoning, no prior-round context.
Record {patch, jury_verdict, jury_review_id, one_line_reason}. Print a one-line result
per patch (PASS → eligible / KILL → refused: <reason>).
The producer may have written an advisory pre-screen into the manifest to help the human read the REPORT — ignore it for the landing decision. Only this fresh verdict counts.
For each patch that PASSED Step 1 and was named by the user:
.aris/meta/backups/<date>/<target> (use the Write tool
to copy contents; corpus paths are not Bash-writable when corpus_write_guard is
active — and the applier should use Write/Edit for corpus mutation anyway).python3 "$PROVENANCE" stamp "$TARGET" --author "$AUTHOR" \
--reviewer "$JURY_MODEL" --verdict-id "$JURY_REVIEW_ID"
stamp() re-asserts cross-family and refuses on same-family — the structural backstop
at the moment the authorization record is written. The stamp is a process receipt
(who authored, who acquitted-at-landing, content hash) — NOT a claim the change is
correct..aris/meta/optimizations.jsonl:
{ts, patch, target, author_model, reviewer_model, jury_review_id, applied: true}.Per patch: LANDED <target> (+ backup path + provenance sidecar) or
REFUSED <patch>: <reason>. Remove landed patches from .aris/meta/pending/. Remind the
user a landed patch is revertable from its backup, and to test the changed skill next run.
A stamp records that a change passed a process (cross-model jury at landing + human landing), not that it is correct. To prevent "approved-but-wrong with a stamp that vouches for it" (false-authority laundering — worse than no stamp, because a later auto-curator reads it as evidence):
verdict_id (auditable review) + content_hash (a later hand-edit
invalidates it).assert_cross_family must not raise. A
deterministic:<verifier> reviewer is valid per skill-governance.md.corpus_write_guard hook (if installed) additionally denies Bash corpus writes — it
does NOT gate Write/Edit, so it does not by itself stop this skill from editing the
corpus; the jury-at-landing + stamp discipline above is what governs Write/Edit
mutations (that discipline is procedure, not a hook-enforced mechanism)..aris/meta/pending/;
invents nothing of its own.Save each landing-jury reviewer call's trace per
review-tracing.md to
.aris/traces/meta-apply/<date>_run<NN>/ — the acquittal that landed a corpus change must
be forensically recoverable.
research
Generate a structured paper outline from review conclusions and experiment results. Use when user says \"写大纲\", \"paper outline\", \"plan the paper\", \"论文规划\", or wants to create a paper plan before writing.
research
Generate a structured paper outline from review conclusions and experiment results. Use when user says "写大纲", "paper outline", "plan the paper", "论文规划", or wants to create a paper plan before writing.
development
Get a deep critical review of research from an external reviewer backend (Codex or manual). Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
research
Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.5 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.