agent-context-budget
Use when an agent task needs bounded context packets, checkpoint-aware compaction, a fresh-session primer, or policy-controlled child results without loading…
Use when reconciled multi-agent output needs an evidence-based PASS, CONDITIONAL PASS, or FAIL record before authorized delivery.
$ npx -y skills add WenyuChiou/agent-collab-skills --skill agent-acceptance-gate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/agent-acceptance-gateContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when reconciled multi-agent output needs an evidence-based PASS, CONDITIONAL PASS, or FAIL record before authorized delivery.
name: agent-acceptance-gate description: Use when reconciled multi-agent output needs an evidence-based PASS, CONDITIONAL PASS, or FAIL record before authorized delivery.
Run the final read-only verification step for a multi-agent round. The gate checks declared criteria and produces acceptance evidence. It never merges, commits, retries, or silently converts a decline, cancellation, timeout, or failure into success.
Historical provider fields may be read for migration only. Current checks use task IDs and roles (`primary-agent`, `delegated-executor`, `reviewer`, and `synthesizer`).
Run the matching preset when a staged diff:
The bundled preset names are `multi-locale-mirror-sync`, `catalog-entry-add`, and `fact-check-frontier-models`.
Presets encode additional failure-mode checks; they do not replace independent review or factual source verification. If a preset cannot run, report a blocker or explicit degraded state. Never claim PASS.
1. Validate the plan, checkpoint, and policy. Unreadable configured policy is a fail-closed blocker. 2. Inventory all tasks and required artifacts. Missing, `null`, declined, cancelled, timeout, degraded, and failed states remain distinct. 3. Verify each declared success criterion with a deterministic command, artifact check, or explicit manual-check requirement. Never invent missing criteria. 4. Compare changed files with declared task scopes and record unexplained drift as FAIL. 5. Check structured statuses, claimed tests, evidence references, reconciliation blockers, and unresolved high-severity risks. 6. Run triggered presets and applicable project invariants. For external facts, cite authoritative sources and mark whether evidence was locally verified. 7. Evaluate canonical policy after the gate cycle. Respect v2 action/slice/goal scope and eligible automatic slice continuation; v1 behavior is unchanged. 8. Write a new immutable acceptance evidence record. Do not overwrite a prior run or human decision. 9. Report acceptance to the primary-agent and user. The host checks existing authorization for delivery; do not demand a fresh approval solely because review, session, or slice changed. A genuinely new human gate still needs a separate recorded decision bound to the specific affected action.
All numeric budgets and retry limits come only from the canonical policy. This skill and its presets must not copy default values.
technically acceptable, not human-authorized to merge.
degraded optional check, or documented non-blocking condition remains.
check failed.
A human may approve a documented exception, but the original gate record stays immutable. The new decision must include gate, actor, decision, timestamp, rationale, and affected action hash. A human override does not rewrite a failed test into a passing test.
Write an acceptance evidence artifact with:
Coordination scratch is gitignored by default. Promote only explicitly required checkpoint snapshots, shipping artifacts, or acceptance evidence into a repository-owned path.
See `references/acceptance_checklist.md` for the verification taxonomy.
繁體中文 · Public harness contract · 0.4 migration · 0.5 goal-slice migration Provider-neutral collaboration skills plus an optional, standard-library-first Python harness for bounded, resumable, human-authorized agent work.
Use when an agent task needs bounded context packets, checkpoint-aware compaction, a fresh-session primer, or policy-controlled child results without loading…
Use when a consequential decision has genuinely competing positions that need bounded adversarial review before a human decision.
Use when multiple agent results must be checked for missing evidence, scope drift, contradictions, and merge conflicts before an acceptance decision.
Use when one agent must iteratively plan, act, evaluate evidence, and revise under a readable policy until acceptance, checkpoint, stop, or human escalation.
Use when a user asks to inspect coordination memory, propose a durable decision or resolution, review pending memory proposals, or apply an explicitly approved…
Use when a goal needs multiple bounded agent roles, parallel or dependent task packets, explicit scope partitions, and a provider-neutral DAG. Not for…