story: e09s04

SkillProductivity

Lets your agent run several subagents in parallel on independent tasks instead of handling them one by one.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the story: e09s04 skill

About this skill

Dispatch multiple subagents in parallel on independent tasks. No waiting between them, all run concurrently. Use when tasks are truly decoupled and speed matters. Distinct from delegate-task (concurrent here, no inter-task review gate).

What this skill tells your AI

The instructions your AI receives, as published by danielvm-git/bigpowers in skills/dispatch-agents/SKILL.md and read by ahel’s review.

story: e45s38

story: e45s30

Dispatch Agents

HARD GATE — HARD GATE — Agent work must be parallelizable and have explicit synchronization points. Do NOT dispatch work that has hidden dependencies between agents.

Run multiple subagents in parallel on independent tasks. Use when tasks are genuinely decoupled — no agent needs the output of another to start.

Distinct from delegate-task: This skill maximizes throughput via concurrency. There is no sequential review gate between tasks. Use delegate-task instead when a single task needs careful two-stage oversight before proceeding.

When to use

  • Tasks that can run simultaneously without shared state
  • Large plans that can be broken into parallel workstreams
  • Exploration: gather information from multiple parts of the codebase at once

When NOT to use

  • Task B depends on Task A's output
  • You need to review Task A before Task B can start safely
  • The tasks share a file and concurrent edits would conflict

Process

1. Confirm independence

Before dispatching, verify each task pair is truly independent:

  • No shared files being written
  • No shared state (DB migrations, config files)
  • No ordering dependency between outcomes

If any two tasks conflict, sequence them with delegate-task or execute-plan instead.

Subagent depth tiers (e45s30)

Map effort: frontmatter and story risk: to prompt depth — do not send minimal_decisive agents a full_maturity brief.

TierWhenBrief shapeToken budget
full_maturityeffort: heavy, risk: P0, security-sensitive diffsFull task_brief + CONVENTIONS excerpts + threat model if presentFull envelope
standardeffort: standard, risk: P1–P2Standard task_brief fields belowDefault
minimal_decisiveeffort: light, risk: P3, read-only explorationgoal + verify + in_scope only≤15 lines

Record depth: <tier> in the Agent tool description when dispatching.

2. Write typed task briefs (Orca message protocol)

Before writing briefs, read specs/state.yaml if it exists — each agent gets only the decisions relevant to its task, nothing else.

Every inter-agent message uses a typed envelope — no freeform prose between waves:

typeWhenRequired fields
task_briefDispatchtask_id, goal, in_scope, out_of_bounds, verify, prior_decisions
checkpointMid-wave progresstask_id, status (running|blocked), comment (one line)
resultAgent returntask_id, exit (pass|fail), summary, verify_output
circuit_open3 consecutive failurestask_id, failures (3), escalate_to (user)

Example task_brief (each agent starts cold — brief size directly controls token cost and hallucination risk):

type: task_brief
task_id: agent-1
goal: [one sentence — what success looks like]
in_scope: [explicit file or module list]
out_of_bounds: [what NOT to touch]
verify: [runnable command]
prior_decisions: [relevant entries from specs/state.yaml — omit if none]

Emit checkpoint comments when an agent is slow or blocked — one line, no stack traces. Parent reads checkpoints before spawning follow-ups.

Do not include the full conversation, full file contents, or decisions unrelated to this agent's task.

3. Circuit breaker + iterative retrieval (max 3 cycles)

Track consecutive failures per task_id. On the 3rd consecutive result.exit: fail for the same task, emit type: circuit_open and stop dispatching that task — escalate to user with the three failure summaries. Reset counter on any pass.

After each wave completes:

  1. Dispatch — run parallel agents with typed task_brief envelopes.
  2. Evaluate — read result messages; list gaps vs goal; honor open circuits.
  3. Refine — tighten briefs or spawn follow-up agents (max 3 cycles total).

Stop when gaps empty, circuit opens, or cycle 3 reached — escalate to user. If agents go silent without returning, invoke diagnose-stall before spawning another wave.

4. Dispatch in parallel

Spawn all agents in a single message using multiple Agent tool calls. Each agent gets its own complete brief.

Agent 1: brief for task A
Agent 2: brief for task B
Agent 3: brief for task C

5. Collect and review results

When all agents return: review each result, run verify commands, check diffs for scope violations.

6. Integrate

Merge accepted results. Resolve conflicts manually; note in summary.

Report: which tasks succeeded, which need revision, overall verify status.

Verify

→ verify: test -f skills/dispatch-agents/SKILL.md && test -f scripts/lib/completeness-critic.sh

Signals

GitHub stars
248
Forks
19
Last commit
Sep 2026
Advanced
Item type
skill
Key
dispatch-agents
Source
github.com/danielvm-git/bigpowers