mcpbeat Sign in

Meta Apply Agent Skill

Privileged applier that LANDS meta-optimize / corpus-audit patches the user approved, with a fresh landing review and human approval. Base Codex review is same-family provisional. Use when the user says \"meta apply\", \"/meta-apply\", \"land the staged patches\", \"应用优化\", after a /meta-optimize run.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
14221
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill meta-apply

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting
Write writes files
Edit edits files in place

The instruction itself

11 sections, as written by the author

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

> Codex assurance: a base landing review records

> review_independence: same-family and acceptance_status: provisional via

> stamp-provisional; the artifact is not auto-curatable. Only an overlay or

> deterministic verifier may produce accepted authorization.

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 fresh landing reviewer 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.

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:

  • The human named THIS patch. Apply only patches the user listed (/meta-apply 1,3

or all); default to applying nothing.

  • Fresh landing review PASS, obtained now. Spawn a fresh gpt-5.6-sol

reviewer via spawn_agent (reasoning_effort: ultra, 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 reviewer-PASSED survivors.

  • Record the review class honestly. Base Codex review is same-family and

lands only with stamp-provisional; it can complete this explicit

human-invoked operation but does not authorize future auto-curation. A

Claude/Gemini overlay or deterministic verifier uses strict stamp and may

record accepted. See

skill-governance.md.

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 through the Codex manifest:

if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
  ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
PROVENANCE=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/provenance.py" ] && PROVENANCE="$ARIS_REPO/tools/provenance.py"
[ -z "$PROVENANCE" ] && [ -f tools/provenance.py ] && PROVENANCE="tools/provenance.py"
[ -n "$PROVENANCE" ] || { echo "ERROR: provenance.py unresolved" >&2; exit 1; }

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

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.

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

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

  • 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).

  • Apply the diff by Edit/Write on the target corpus file.
  • Stamp provenance on the changed file. Base Codex uses:
   python3 "$PROVENANCE" stamp-provisional "$TARGET" --author "$AUTHOR" \
     --reviewer "$JURY_MODEL" --verdict-id "$JURY_REVIEW_ID"

This records review_independence: same-family and

acceptance_status: provisional; is_auto_curatable remains false. If the

active overlay produced a cross-family result, use strict stamp instead.

  • Log to .aris/meta/optimizations.jsonl:

{ts, patch, target, author_model, reviewer_model, jury_review_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* (fresh landing review + 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.

  • Never promote provisional to accepted. Base Codex always uses

stamp-provisional; only an overlay or deterministic verifier may use strict

stamp.

  • 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 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.

Other skills for the same job

different authors, same section of the catalogue
Protocolsio Integration
by christophacham
×4

Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.

16k tokens
Tailored Resume Generator
by frostant
×4

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances

3k tokens
Excalidraw Diagram Generator
by github
vendor ×3

Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.

36k tokens scripts
Expo Dev Client
by openai
vendor ×3

Build and distribute Expo development clients locally or via TestFlight

961 tokens
Executing Plans
by ZhanlinCui
×3

Use when you have a written implementation plan to execute in a separate session with review checkpoints

542 tokens
Anndata
by christophacham
×3

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

16k tokens
Benchling Integration
by christophacham
×3

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

14k tokens
Biopython
by christophacham
×3

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

24k tokens

How to use it

Copy the folder

Take wanshuiyin/auto-claude-code-research-in-sleep-meta-apply from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.