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meta-apply

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

Install / Use

npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

93/100

Supported Platforms

OpenAI Codex

Our assessment of meta-apply

meta-apply scores 93/100 on our quality scale, 354th of 2,855 Development & Engineering skills we index (top 13%).

Its SKILL.md is 7.7 KB long, well organised into 11 sections with 1 code example: a thorough specification that gives an agent plenty to work with.

With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
17/20
Description
15/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 9 days ago, so meta-apply is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

meta-apply compared with similar skills

All 4 of these similar skills score higher than meta-apply; compare them before choosing.

SkillScoreStarsUpdatedFormat
meta-apply (this skill)by wanshuiyin9316.6k9d agoSKILL.md
Agent-Reachby Panniantong10085.8k12d agoCLAUDE.md
headroomby headroomlabs-ai10074.0k1d agoCLAUDE.md
rufloby ruvnet10073.4ktodayCLAUDE.md
CowAgentby zhayujie10047.1ktodayCLAUDE.md

Frequently asked questions

How do I install meta-apply?
Run npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply. The install tabs above show the steps for each supported agent.
Which AI agents does meta-apply work with?
It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
Is meta-apply safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is meta-apply still maintained?
The repository was last updated 9 days ago, so meta-apply is actively maintained.

name: meta-apply description: "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." argument-hint: "[patch-number-or-all]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply

Meta-Apply: the privileged landing gate for self-modification patches

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 acquittal is generated HERE, not relayed

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.

The non-negotiable rules (jury-at-landing, reject-default)

For each staged patch the user asks to land, in order — any failure ⇒ skip & report, never silently apply:

  1. The human named THIS patch. Apply only patches the user listed (/meta-apply 1,3 or all); default to applying nothing.
  2. Fresh cross-model jury PASS, obtained now. Run mcp__codex__codex (fresh thread, NOT codex-reply; model: gpt-6-astra, config: {"model_reasoning_effort": "ultra"}, 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. Include the scope-limits block from review-scope-limits.md in that prompt: this jury judges ARIS's own mechanism, so an over-defensive KILL permanently blocks a good patch. Note the block bans proposing new hash binding — it is not a reason to KILL a patch that touches the existing provenance stamp. 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.)
  3. Author ≠ reviewer family. The author is the producer's executor model; the reviewer is the codex model that just judged it. Run 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.)

Workflow

Step 0: Load staging + resolve the helper

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 4-layer chain in integration-contract.md §2 (.aris/tools/ → tools/ → $ARIS_REPO/tools/ → $ARIS_REPO/tools/ via ~/.aris/repo).

Step 1: Jury-at-landing for each requested patch

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.

Step 2: Land the survivors (Write/Edit only — never Bash)

For each patch that PASSED Step 1 and was named by the user:

  1. Back up the target to .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).
  2. Apply the diff by Edit/Write on the target corpus file.
  3. Stamp provenance on the changed file:
    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.
  4. Log to .aris/meta/optimizations.jsonl: {ts, patch, target, author_model, reviewer_model, jury_thread_id, applied: true}.

Step 3: Report

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.

Provenance is a receipt, not an acquittal of correctness

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):

  • The stamp carries verdict_id (auditable review) + content_hash (a later hand-edit invalidates it).
  • Recommended (not yet built): a TTL forcing re-review of long-lived auto-authored artifacts, and a behavioral auditor that REVOKES a stamp when a landed skill misbehaves. Track as follow-up; never treat a stamp as permanent truth.

Key Rules

  • Human-invoked only. Never run as a side-effect of another skill or a hook.
  • Jury-at-landing, reject-default, no override. The binding verdict is produced HERE on the staged diff; never trust a producer-written verdict; the human picks among survivors, never resurrects a KILL.
  • Cross-family or refuse. assert_cross_family must not raise. A deterministic:<verifier> reviewer is valid per skill-governance.md.
  • Corpus mutation goes through Write/Edit (reviewable, attributable), not Bash. The 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).
  • Back up before every mutation. Reversible by construction.
  • Only land staged patches. Applies what producers staged in .aris/meta/pending/; invents nothing of its own.

Review Tracing

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.

Related Skills

View on GitHub
GitHub Stars16.6k
CategoryDevelopment
Updated9d ago
Forks1.4k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions