agent-context-budget — Claude Code skill: context limits
This skill bounds subagent replies, moves large outputs to files, and prepares handoffs when a session is nearing its context limit.
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a claude-codeWhat agent-context-budget does
The skill gives parallel and background agents a compact return shape: status, result, changed files, checks, risks, and blockers. Full logs, transcripts, and diffs stay at file paths, while reading tasks return conclusions with file:line references. It assigns a cheaper model tier to mechanical work and a stronger tier to review, root-cause analysis, security, and governance.
Before the active context runs out, it writes a fresh-session primer covering the goal, decisions, open questions, evidence paths, and next step. Harness mode validates policy and checkpoint state before each child spawn and after each result. Durable decisions become proposal files under .coord/memory-proposals/<proposal-id>.json; an unapproved proposal is never treated as canonical.
Harness mode requires AGENT_COLLAB_POLICY and an available agent-collab package. If that package cannot be used, the autonomous loop does not start and the skill prepares packets for human-controlled execution.
What it helps with
- cap a subagent's returned output
- store full logs and diffs by path
- assign model tiers before launching lanes
- prepare a session handoff before context runs out
- record a decision as an evidence-backed proposal
Example prompts for the agent
- Launch parallel reading agents and return only conclusions with
file:linereferences. - Put the full transcript in a file and return its path with a short handoff.
- Evaluate the checkpoint before the next child spawn and prepare a fresh-session primer.
How to install agent-context-budget
1Skills CLI (Vercel)
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a claude-code2Manually: put the files in place
~/.claude/skills/agent-context-budget/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/agent-context-budget/
1Skills CLI (Vercel)
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a codex2Manually: put the files in place
.agents/skills/agent-context-budget/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a cursor2Manually: put the files in place
.cursor/skills/agent-context-budget/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install WenyuChiou/agent-collab-skills agent-context-budget2Manually: put the files in place
.github/skills/agent-context-budget/SKILL.md1Skills CLI (Vercel)
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a gemini-cli2Platform CLI
gemini skills install https://github.com/WenyuChiou/agent-collab-skills --path skills/agent-context-budgetManually: put the files in place
.devin/skills/agent-context-budget/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about agent-context-budget
How do I install agent-context-budget in Claude Code?
Run npx skills add WenyuChiou/agent-collab-skills --skill agent-context-budget -a claude-code, then check ~/.claude/skills/agent-context-budget/SKILL.md.
What does agent-context-budget do?
It limits inline subagent results, routes long artifacts to files, and prepares checkpoint-based handoffs as context is consumed. It also separates model tiers for mechanical work and review.
What does harness mode require?
Harness mode uses AGENT_COLLAB_POLICY and the agent-collab package. When the package is unavailable, the autonomous loop is not started.
Source text
SKILL.md · MIT
---
name: agent-context-budget
description: Use when launching parallel or background subagents, or when a handoff, log, or result would be more than a short summary (roughly 2 KB). Caps return sizes (summary in the reply, detail by path), picks a cheap model tier for mechanical lanes, records durable decisions as proposals, and checkpoints or primes a fresh session before context runs out.
---
# agent-context-budget
Keep the primary session small while lanes do the reading and writing. There
is no second budget source: native mode follows the host's limits and the caps
written into each brief; harness mode takes every number from the policy
referenced by the current `TaskCheckpoint`. The "roughly 2 KB" in the
description says when to use this skill; it is not a limit.
Do not use this skill to summarize away unresolved failures, human decisions,
raw scientific evidence, or security findings.
## Before launching lanes
- Put a return cap in every brief: status, short summary, files changed,
checks run, risks, blockers. Long output goes to a file and the lane returns
the path. Raw logs, transcripts, and full diffs never come back inline.
- Reading lanes return conclusions with `file:line` references, not file
dumps. Name exact files and line ranges in the brief.
- Choose model tiers before launch: a cheap tier for mechanical lanes (pattern
edits, scaffolds, bulk reads), a strong tier for review, verdicts,
root-cause analysis, security, and governance. Never switch a lane's model
silently after a failure.
- Background lanes notify on completion. Keep working and read the result
then; do not poll.
- For a follow-up, continue the lane that already holds the context instead
of starting a fresh one.
Host mechanics (parallel calls, background runs, per-lane model, return
schemas) are in [native lanes](../../docs/native-lanes.md).
## What goes into a brief
Only decision-relevant material:
- goal and acceptance contract;
- explicit files and tools in scope;
- direct evidence paths;
- current recorded human decisions;
- relevant interfaces or schemas.
Keep raw logs and complete artifacts at their paths. Include a short failure
excerpt only when the next action needs it.
## Before context runs out
Write a fresh-session primer: goal, current state, decisions and who made
them, open questions, evidence paths, next step. Keep unresolved failures,
human decisions, and security findings visible. Compaction changes the active
context, not cumulative usage or failure history.
## Durable decisions (formerly agent-shared-memory)
Memory output is proposal-only. Repository state, current evidence, and
recorded human decisions outrank memory; recall systems are optional caches.
Native mode may write a proposal file and nothing else under `.coord/`;
applying an approved proposal needs the signed approval of harness mode.
- Write a proposal to `.coord/memory-proposals/<proposal-id>.json` with an
immutable `action` (operation, target_ref, summary, evidence_refs).
- Supersede, archive, and delete need a specific `target_ref`. A proposal
with no evidence refs stays pending.
- Only an explicit human approval, recorded with actor, decision, timestamp,
rationale, affected action hash, and trusted-host HMAC authorization, lets a
proposal be applied. Decline, timeout, and revise are not approval.
- Applying appends one new event to `.coord/memory.yml`.
It never edits an existing event; a resolution, correction, or
supersession is a new append-only event.
- Never report an unapproved proposal as a canonical decision. Delegated
executors never receive the signing key.
Full schema and apply rules:
[references/coord_memory_schema.md](references/coord_memory_schema.md).
## Harness mode
Applies when `AGENT_COLLAB_POLICY` is configured, or a round spans sessions or
tools, runs unattended, or must resume.
1. Validate:
agent-collab policy validate --policy <policy-ref> --json
agent-collab checkpoint validate --checkpoint <checkpoint-ref> --json
2. Record the observed metrics in the checkpoint. Never estimate a lower
number to fit the policy.
3. Before each child spawn and after each returned result, run:
agent-collab policy evaluate \
--policy <policy-ref> \
--checkpoint <checkpoint-ref> \
--json
4. Obey the decision:
- `continue` with `spawn_allowed=true`: build the bounded packet.
- `checkpoint`: write the checkpoint and primer. A v2 slice with
auto_continue advances atomically under the same goal; v1 returns
control. For a v2 action-scope `context_compaction_required`, do local
context maintenance without asking for renewed approval: keep full
artifacts, acceptance evidence, failures, and authorization; build a
smaller linked packet or use native compaction; record measured sizes;
re-evaluate. Do not spawn or advance a slice while this gate is pending.
- `stop` or `spawn_allowed=false`: do not spawn. Respect the v2 scope; an
exhausted action still allows safe primary-agent diagnosis.
5. A configured but unreadable policy stops the run. If the harness package is
unavailable, do not start an autonomous loop; prepare packets for
human-controlled execution instead.
Multi-cycle loop rules (retry counting, failure identity, no-progress cycles)
are in [references/harness-loop.md](references/harness-loop.md).
Outputs (scratch by default): `.coord/context_<NNN>.md` (shape in
[references/harness-loop.md](references/harness-loop.md)),
`.coord/session_primer.md`, and the updated checkpoint. Promote a checkpoint
snapshot only when the repository needs it for resume, shipping, or
acceptance evidence. Agent boundaries do not imply commits.
## Invariants
- Policy values have one machine-readable source; the stricter of the policy
and the host's native limit wins.
- Reserve capacity for required independent review.
- No silent retry, model switch, context discard, or result truncation. A
capped return that points to the full output is not truncation.
- Missing evidence is reported as missing. A decline, cancellation, timeout,
error, or `null` result remains non-success.
- Recall systems may suggest context; they never override repository state or
recorded human decisions.
© WenyuChiou · MIT · WenyuChiou/agent-collab-skills