agent-acceptance-gate
Use when reconciled multi-agent output needs an evidence-based PASS, CONDITIONAL PASS, or FAIL record before authorized delivery.
Use when an agent task needs bounded context packets, checkpoint-aware compaction, a fresh-session primer, or policy-controlled child results without loading raw logs into the primary session.
$ npx -y skills add WenyuChiou/agent-collab-skills --skill agent-context-budget --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/agent-context-budgetContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when an agent task needs bounded context packets, checkpoint-aware compaction, a fresh-session primer, or policy-controlled child results without loading raw logs into the primary session.
name: agent-context-budget description: Use when an agent task needs bounded context packets, checkpoint-aware compaction, a fresh-session primer, or policy-controlled child results without loading raw logs into the primary session.
Prepare bounded context for a task without inventing a second budget source. Numeric limits come from the policy referenced by the current TaskCheckpoint. The public contract and commands are documented in ../../docs/public-harness-contract.md.
Do not use it to summarize away unresolved failures, human decisions, raw scientific evidence, or security findings.
If the policy is configured but unreadable, stop. If the optional agent-collab-harness package is unavailable, do not start an autonomous loop or child spawn; prepare the packet for human-controlled execution instead.
1. Validate policy and checkpoint:
agent-collab policy validate --policy <policy-ref> --json agent-collab checkpoint validate --checkpoint <checkpoint-ref> --json
2. Record current observed metrics in the checkpoint. Do not estimate a lower number to fit the policy. 3. Run policy evaluation before a child spawn:
agent-collab policy evaluate \ --policy <policy-ref> \ --checkpoint <checkpoint-ref> \ --json
4. Obey the result:
advances atomically and resumes under the same goal; v1 returns control. For v2 scope=action with context_compaction_required, perform local context maintenance without asking for renewed approval: preserve full artifacts, acceptance evidence, failures, and authorization; build a smaller linked packet or use native compaction, record measured sizes, then re-evaluate. Do not spawn or advance a slice while this context gate is pending.
exhaustion of one action does not prohibit safe primary-agent diagnosis. 5. Put only decision-relevant material in the packet:
6. Keep raw logs and complete artifacts at paths. Include a short failure excerpt only when it is needed to diagnose the next action. 7. After a result returns, update observed metrics and evaluate again.
These are scratch artifacts by default. Promote a checkpoint snapshot only when the repository explicitly needs it for resume, shipping, or acceptance evidence. Agent boundaries do not imply commits.
Policy
Goal <one bounded objective>
Recorded decisions
Evidence
Scope
Acceptance
Return contract
accepted evidence never reset. Retain the stricter explicit native/host limit.
release active concurrency; the host's actual capacity also limits spawning.
If safe compaction is unavailable, report that limitation, not a generic request to continue. Never clear an ambiguous legacy blocker automatically.
recorded human decisions.
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 reconciled multi-agent output needs an evidence-based PASS, CONDITIONAL PASS, or FAIL record before authorized delivery.
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…