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/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 raw logs into the primary session.

From plugin
agent-collab-workspace
277 skills
Install
$ npx -y skills add WenyuChiou/agent-collab-skills --skill agent-context-budget --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/agent-context-budget

Context 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.

SKILL.md

agent-context-budget.SKILL.md
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.

agent-context-budget

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.

Use this skill for

  • A task that may spawn a delegated executor or reviewer.
  • A transcript approaching its configured checkpoint boundary.
  • A fresh session that needs a compact, evidence-linked primer.
  • Results or logs that are too large for the primary-agent context.

Do not use it to summarize away unresolved failures, human decisions, raw scientific evidence, or security findings.

Inputs

  • Goal and task id.
  • Readable agent policy.
  • Current TaskCheckpoint.
  • Evidence and artifact paths needed by the next role.
  • Optional existing .coord/plan.yml and approved memory events.

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.

Workflow

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:

  • continue and spawn_allowed=true: create the bounded task packet.
  • checkpoint: write a checkpoint/primer. A v2 slice with auto_continue

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.

  • stop or spawn_allowed=false: do not spawn. Respect v2 decision scope;

exhaustion of one action does not prohibit safe primary-agent diagnosis. 5. Put only decision-relevant material in the packet:

  • goal and acceptance contract
  • explicit files and tools in scope
  • direct evidence references
  • current recorded human decisions
  • relevant interfaces or schemas

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.

Outputs

  • .coord/context_<NNN>.md: round-specific packet plan.
  • .coord/session_primer.md: bounded fresh-session digest.
  • Updated TaskCheckpoint at the plan's checkpoint_ref.

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.

Packet shape

Context packet — <task id>

Policy

  • policy_ref: <path>
  • policy_hash: <sha256>
  • checkpoint_ref: <path>

Goal <one bounded objective>

Recorded decisions

  • <gate / actor / decision / action hash>

Evidence

  • <path or stable source locator>

Scope

  • may read: <paths>
  • may write: <paths>
  • must not touch: <paths>

Acceptance

  • <runnable check>

Return contract

  • concise summary
  • files changed
  • tests run
  • risks and blockers
  • raw logs by path only

Invariants

  • Policy values have one machine-readable source.
  • v2 slice counters reset only through checkpoint advance; goal totals and

accepted evidence never reset. Retain the stricter explicit native/host limit.

  • Reserve child capacity for required independent review. Completed children

release active concurrency; the host's actual capacity also limits spawning.

  • No silent retry, model switch, context discard, or result truncation.
  • Compaction changes active context, not cumulative usage or failure history.

If safe compaction is unavailable, report that limitation, not a generic request to continue. Never clear an ambiguous legacy blocker automatically.

  • Missing evidence is reported as missing, not summarized as success.
  • A decline, cancellation, timeout, error, or null result remains non-success.
  • Recall systems may suggest context; they do not override repository state or

recorded human decisions.

Read more
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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.

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Repo: WenyuChiou/agent-collab-skills

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