skills/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 thread, 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.mcp__codex__codex (fresh thread,
NOT codex-reply; model: gpt-5.5, reasoning: xhigh, sandbox: 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. (Here it always holds: producer=Claude,
jury=codex. The check is the structural backstop.)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/ → $ARIS_REPO/tools/).
For every patch the user asked to land, read its staged .diff and target, then run the
fresh codex jury (Rule 2) — paths-only, no producer reasoning, no prior-round context.
Record {patch, jury_verdict, jury_thread_id, one_line_reason}. Print a one-line result
per patch (PASS → eligible / KILL → refused: <reason>).
The producer may have written an advisory pre-screen into the manifest to help the human read the REPORT — ignore it for the landing decision. Only this fresh verdict counts.
For each patch that PASSED Step 1 and was named by the user:
.aris/meta/backups/<date>/<target> (use the Write tool
to copy contents; corpus paths are not Bash-writable when corpus_write_guard is
active — and the applier should use Write/Edit for corpus mutation anyway).python3 "$PROVENANCE" stamp "$TARGET" --author "$AUTHOR" \
--reviewer "$JURY_MODEL" --verdict-id "$JURY_THREAD_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_thread_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 codex 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.
data-ai
Generate and rank research ideas given a broad direction. Use when user says "找idea", "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.
development
Get a deep critical review of research from GPT using a secondary Codex agent. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
data-ai
Generate and rank research ideas given a broad direction. Use when user says "找idea", "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.
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Autonomous multi-round research review loop. Repeatedly reviews using a secondary Codex agent, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.