
Meta Apply
FreeControl the landing of self-modification patches securely.
Free · Opens the source repo
What Meta Apply does
Meta Apply is a specialized skill designed to manage the application of self-modification patches in a controlled and secure manner. It acts as a privileged gatekeeper that ensures only user-approved patches are applied to the system. The skill is invoked manually by the user, who must explicitly state their intent to apply specific patches after reviewing the associated reports. This human-invoked process is crucial for maintaining oversight and preventing unauthorized changes to the corpus.
The skill operates by enforcing a strict privilege boundary between the patch proposal and the actual application. It separates the stages of patch staging and landing, ensuring that only a human can authorize the transition from a proposed patch to an applied change. This is achieved through a two-step review process where each staged patch is evaluated by a fresh reviewer at the time of landing. The reviewer assesses whether the proposed change improves the system without introducing regressions, and only those patches that receive a 'PASS' verdict can be applied.
In addition to ensuring that only approved patches are applied, Meta Apply also maintains a detailed log of all actions taken, including backups of the original files and the provenance of each change. This logging provides an auditable trail that can be referenced in the future, ensuring accountability and traceability of modifications made to the system. The skill is particularly useful for developers and designers who need to manage complex systems and require a robust mechanism for applying changes while minimizing risks.
Overall, Meta Apply is an essential tool for those who prioritize security and oversight in their development processes, allowing them to confidently manage self-modification without sacrificing control or integrity.
When to use it
Use Meta Apply when you need to apply approved patches to your system after a thorough review process.
When not to use it
This skill is not suitable for automated patch applications or scenarios where human oversight is not feasible.
What you can build with it
Applying Approved Patches
After running the /meta-optimize command, use Meta Apply to manually apply the approved patches, ensuring controlled modifications.
Reviewing Patch Impact
Before applying any changes, utilize the skill to review the impact of each patch through a fresh reviewer, maintaining system integrity.
Maintaining Change Audit Trails
Use Meta Apply to log all applied patches and their provenance, creating an auditable trail for future reference.
How to install Meta Apply
View source1. Install with the skills CLI
npx skills add wanshuiyin/auto-claude-code-research-in-sleep/meta-apply --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by wanshuiyinMeta-Apply: the privileged landing gate for self-modification patches
Codex assurance: a base landing review records
review_independence: same-familyandacceptance_status: provisionalviastamp-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 1,3orall); default to applying nothing. - Fresh landing review PASS, obtained now. Spawn a fresh
gpt-5.6-solreviewer viaspawn_agent(reasoning_effort: ultra, read-only, paths-only perreviewer-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 strictstampand may record accepted. Seeskill-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 whencorpus_write_guardis 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:
This recordspython3 "$PROVENANCE" stamp-provisional "$TARGET" --author "$AUTHOR" \ --reviewer "$JURY_MODEL" --verdict-id "$JURY_REVIEW_ID"review_independence: same-familyandacceptance_status: provisional;is_auto_curatableremains false. If the active overlay produced a cross-family result, use strictstampinstead. - 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 strictstamp. - Corpus mutation goes through Write/Edit (reviewable, attributable), not Bash. The
corpus_write_guardhook (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.
Frequently asked questions about Meta Apply
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