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…

MITAuto-check: notes

Install Meta Apply

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

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep meta-apply --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/meta-apply .claude/skills/meta-apply && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
meta-apply
GitHub stars
17k
Token cost
~2k tokens
SKILL.md length
923 words
Files
1
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 4 steps: Load staging + resolve the helper → Jury-at-landing for each requested patch → Land the survivors (Write/Edit only —… → …
  • The user says meta apply
  • SKILL.md covers The acquittal is generated…, The non-negotiable rules…, Workflow and Provenance is a receipt, not…, plus 2 more sections
  • Calls python3

What it does

Meta Apply is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. 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.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework… The licence is MIT.

When your agent uses it

  • The user says meta apply
  • Land the staged patches
  • After a /meta-optimize run

Example prompts

  • “meta apply”
  • “/meta-apply”
  • “land the staged patches”
  • “/meta-apply”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Load staging + resolve the helper
  2. Jury-at-landing for each requested patch
  3. Land the survivors (Write/Edit only — never Bash)
  4. Report

What it can do on your machine

Read from SKILL.md and the folder at commit 26b95cf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • mcp__codex__codex
    • mcp__codex__codex-reply

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Meta Apply loads about 2k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 923 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wanshuiyin/Auto-claude-code-research-in-sleep at commit 26b95cf, republished under its MIT licence (© wanshuiyin). 923 words, ~1,964 tokens.

Download SKILL.mdSave it as .claude/skills/meta-apply/SKILL.md (or your agent's skills folder).
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.
allowed-tools
Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply
argument-hint
[patch-number-or-all]

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
bash
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.

Show full SKILL.md (430 more words)Show less
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:
    bash
    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.

© wanshuiyin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/meta-apply of wanshuiyin/Auto-claude-code-research-in-sleep.

Open the folder on GitHubat commit 26b95cf

Compare with similar skills

Meta Apply next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Meta Apply compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Meta Apply this skillwanshuiyin/Auto-claude-code-research-in-sleep17k—~2kAutomated safety check: NotesMIT
Landing Optimizeraaron-he-zhu/aaron-marketing-skills2.9k—~3.3kAutomated safety check: PassApache-2.0
Landingalirezarezvani/claude-skills28k—~3.8kAutomated safety check: PassMIT
SQL Optimizationgithub/awesome-copilot40k2 repos~2.3kAutomated safety check: PassMIT
Landing Optimizeraiskillstore/marketplace433—~2.3kAutomated safety check: PassApache-2.0
Agent Performance Optimizerruvnet/ruflo74k2 repos~3.6kAutomated safety check: PassMIT

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Questions about Meta Apply

What does Meta Apply do?

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…. Meta Apply is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. 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.

When should I use Meta Apply?

Meta Apply fits situations like: the user says meta apply; land the staged patches; after a /meta-optimize run.

How do I install Meta Apply in Claude Code?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply -a claude-code`. Or copy the skill folder (skills/meta-apply in wanshuiyin/Auto-claude-code-research-in-sleep) into .claude/skills/meta-apply in your project. Claude Code loads it when a task matches its description.

How do I install Meta Apply in Codex?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply -a codex`. Or copy the skill folder (skills/meta-apply in wanshuiyin/Auto-claude-code-research-in-sleep) into .agents/skills/meta-apply in your project. Codex loads it when a task matches its description.

Can I use Meta Apply in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meta-apply, .gemini/skills/meta-apply, .github/skills/meta-apply and .opencode/skills/meta-apply in your project.

What does Meta Apply need to run?

Going by SKILL.md and its folder, Meta Apply needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply.

Does Meta Apply access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Meta Apply safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Meta Apply use?

Meta Apply is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Meta Apply use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Meta Apply?

Skills that share tags, products or a category with Meta Apply: Landing Optimizer (aaron-he-zhu/aaron-marketing-skills, 2.9k stars), Landing (alirezarezvani/claude-skills, 28k stars), SQL Optimization (github/awesome-copilot, 40k stars) and Landing Optimizer (aiskillstore/marketplace, 433 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meta Apply?

wanshuiyin (a GitHub user) maintains it in wanshuiyin/Auto-claude-code-research-in-sleep, which has 17,205 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 7, 2026.

Source: wanshuiyin/Auto-claude-code-research-in-sleep on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.