agents-swarm-orchestration: Workflow Automation for AI Agents | OpenAgentSkill

Overview

Coordinates multi-agent execution across subagents, teams, and workflows. Use when planning dependency-aware fan-out, verifier passes, runtime selection, or Loop Engineering.

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Swarm Orchestration

Advanced execution layer for multi-worker runs after agent or team selection.

Coordinate multiple workers without polluting the main thread. Use this skill after agents-subagents has already selected the right agent, member, team, or debate pattern. This skill is for choosing the orchestration surface, freezing task ownership before fan-out, and requiring structured outputs that the lead agent can validate and merge safely.

Terminology (Aug 2026)

"Swarm" is community vocabulary — it appears nowhere in Anthropic documentation. Use the official primitive names when writing configs, prompts, or docs; keep "swarm" only as informal shorthand for the whole category.

Informal	Official primitive	Status (Aug 2026)
"swarm of subagents"	Subagents	GA. Background behavior is mode-dependent; `background: true` forces background but `false` is not a documented foreground pin. Recursive spawn currently defaults to three layers below the main session
"swarm with peer chat"	Agent teams	Experimental, env-gated `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1`. Behavior churns weekly — re-verify before relying
"scripted swarm"	Dynamic workflows	Shipped 2026-05. JS in `.claude/workflows/`; ≤1000 agents/run, 16 concurrent. The repeatable-orchestration artifact
"swarm across terminals"	Cross-session messaging	Aug 2026, macOS/Linux. Sessions message each other without a team — lighter than teams for passing findings

Quick Reference

Situation	Default pattern	Why
1-2 tasks or shared-file edits	Stay in the main conversation	Parallelism adds coordination overhead without payoff
Focused worker that only needs to report back	Claude Code subagent or Codex worker	Isolated context, simple coordination
Workers must talk to each other	Claude Code agent team	Shared task list plus direct messaging
Read-heavy scans, tests, triage, summarization	Parallel workers	Keeps noisy intermediate output off the lead thread
One coordinator should retain user ownership	Manager / agents-as-tools	Lead keeps control of decisions and final answer
Specialist should take over the conversation	Handoff	Ownership moves to the specialist agent
Work of unknown extent — discovery is the task	Loop until K empty rounds	A fixed task list cannot be enumerated up front
Loop Engineering: recurring discovery or evaluation	Loop-until-dry or budget-bounded loop	Define convergence, termination, and state checkpoints
Many items, known stages, high intermediate volume	Scripted workflow (Claude Code)	Script holds control flow; lead context holds only the result

references/loop-orchestration.md - Bounded iteration vs retry, loop-until-dry, convergence detection, termination predicates, dedup-target rule
../ai-coding-agents-tasks/references/loop-and-graph-runtime-surfaces.md - Loop Engineering and Graph Engineering runtime comparison: task queues, cyclic graphs, and workflows
references/scripted-workflows.md - Script-held deterministic control flow (Claude Code Workflows): agent/parallel/pipeline, barrier-vs-pipeline, resume and caching
references/platform-patterns.md - Platform guidance for Claude Code subagents, Codex subagents, Codex multi-agents, and OpenAI Agents SDK
references/output-contracts.md - Task schema, worker report schema, and merge contract
references/operational-guardrails.md - Safety, stop conditions, observability, and verification gates
references/cost-discipline.md - Fan-out cost patterns, session lifecycle, loops/schedules audit, orchestration-layer config
references/orchestration-maintenance-runbook.md - How to audit, maintain, and refresh swarm discipline over time

Maintainer note: eight URLs here are intentionally duplicated from ../agents-subagents/data/sources.json (Claude Code subagents, Agent Teams, Codex Multi-Agents, Codex Subagents, both OpenAI Agents SDK pages, OpenAI prompt-caching guide, Karpathy coding notes). Each skill frames those sources for a different reader. When a URL rotates, update both files in the same commit.

When To Use / Not To Use

Use	Do Not Use
`agents-subagents` already chose the team; now needs execution planning	Still deciding which agent, team, or debate mode fits
3+ bounded tasks with clear ownership or dependencies	Tasks share the same file or unresolved interface
Requirements, decisions, synthesis must stay in one lead context	Main blocker is product ambiguity, not execution bandwidth
Exploration, tests, logs, or review can run in parallel	Workers would need the same context and make the same decisions
Loop Engineering needs a bounded multi-pass orchestration contract	One pass or a simple queue already meets the goal
Verification must be explicit, not implied by worker confidence	Work is small enough that orchestration cost exceeds execution cost

Relationship To Agents-Subagents

agents-subagents is the entry point. It selects the mode and prepares the first launch prompt. This skill takes over when the plan needs multi-wave execution, worker dependencies, verifier passes, or merge/conflict control.

Operating Principles

Lead owns requirements, decisions, approvals, and final synthesis — not execution.
Default to read-heavy parallelism; parallel writes are higher-risk.
Freeze shared interfaces before dispatching edit-capable workers.
Give every worker exclusive owned_files and explicit do_not_touch boundaries.
Pass distilled dependency outputs, not raw logs or long transcripts.
Require structured worker reports — the lead validates and merges deterministically.
Re-plan when conflict resolution costs more than the fan-out saved.
Fresh context per worker: each worker brief contains only its task, plan section, file ownership, and interface contracts — not the lead's full history. Prevents context rot; gives each worker a full window.
State in files: task graph, progress, decisions, and dependency outputs live in structured files (frontmatter MD / JSON / YAML). Any new lead session resumes by reading files, not memory.
Checkpoint long runs: snapshot task state, reports, and decisions to checkpoints/ at each wave boundary.
Budget per worker: explicit token/time/tool caps at dispatch. Budget-conservation invariant: child budgets are strict subsets of the parent's remaining budget. Workers that breach their budget stop and escalate — they do not continue. (Ye & Tan, Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems, arXiv:2601.08815, 2026)
Telemetry per worker: assign a run id or span id; log inputs, outputs, status, tokens, and duration to one structured location.
Durable approval channels: route approvals through mailbox/poller with request IDs, not ephemeral callbacks.
Minimum toolset per worker: use tools, disallowedTools, and skills fields to give each worker only what it needs.
Memory opt-in: prefer clean-context workers + file-backed checkpoints. Enable memory only when the role genuinely benefits from cross-run priors; never default it for verifiers or reviewers. Prefer file tools over schema-constrained memory APIs. (Lance Martin, 2026-04-24; ../ai-context-layer/references/filesystem-as-memory.md)

For context rotation and state handoff patterns, see ../ai-agents/references/context-rotation-and-state.md.

Explicit Fan-Out Is The Durable Default

Claude Opus 4.7 (GA 2026-04-16) shipped a lasting behavior change: it spawns fewer subagents by default than 4.6, favoring single-response completion over implicit parallelism. Fan-out workflows that previously worked without being asked — read-heavy scans, multi-file refactors, review waves, cross-repo audits — now silently serialize unless the lead is told to fan out explicitly. Opus 4.8 (current as of this writing) inherits the same conservative default; treat "assume no auto-parallelism" as the standing assumption for whatever frontier model is current, and re-verify against release notes each time the lead model changes.

Anthropic's source guidance is to give the model explicit fan-out instructions; it does not prescribe where the instruction must live. Our repo convention is to install the canonical phrasing once in AGENTS.md / CLAUDE.md (not duplicated per launch prompt):

Spawn multiple subagents in the same turn when fanning out across items or reading multiple files. Do not spawn a subagent for work you can complete in a single response.

Full guidance and source links live in agents-subagents. Judgment call for the lead: after any model swap, run one throwaway fan-out task and watch whether it parallelizes

View source ↗

File metadata

name: agents-swarm-orchestration
description: "Coordinates multi-agent execution across subagents, teams, and workflows. Use when planning dependency-aware fan-out, verifier passes, runtime selection, or Loop Engineering."
compatibility: Claude Code + Codex. Claude Code Agent tool (renamed from Task in v2.1.63) plus Codex subagents — runtime-specific dispatch.
version: "1.5"
last_validated: 2026-08-27

View original text

---
name: agents-swarm-orchestration
description: "Coordinates multi-agent execution across subagents, teams, and workflows. Use when planning dependency-aware fan-out, verifier passes, runtime selection, or Loop Engineering."
compatibility: Claude Code + Codex. Claude Code Agent tool (renamed from Task in v2.1.63) plus Codex subagents — runtime-specific dispatch.
version: "1.5"
last_validated: 2026-08-27
---

# Swarm Orchestration

Advanced execution layer for multi-worker runs after agent or team selection.

Coordinate multiple workers without polluting the main thread. Use this skill after `agents-subagents` has already selected the right agent, member, team, or debate pattern. This skill is for choosing the orchestration surface, freezing task ownership before fan-out, and requiring structured outputs that the lead agent can validate and merge safely.

## Terminology (Aug 2026)

"Swarm" is community vocabulary — it appears nowhere in Anthropic documentation. Use the official primitive names when writing configs, prompts, or docs; keep "swarm" only as informal shorthand for the whole category.

| Informal | Official primitive | Status (Aug 2026) |
|----------|-------------------|-------------------|
| "swarm of subagents" | **Subagents** | GA. Background behavior is mode-dependent; `background: true` forces background but `false` is not a documented foreground pin. Recursive spawn currently defaults to three layers below the main session |
| "swarm with peer chat" | **Agent teams** | Experimental, env-gated `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1`. Behavior churns weekly — re-verify before relying |
| "scripted swarm" | **Dynamic workflows** | Shipped 2026-05. JS in `.claude/workflows/`; ≤1000 agents/run, 16 concurrent. The repeatable-orchestration artifact |
| "swarm across terminals" | **Cross-session messaging** | Aug 2026, macOS/Linux. Sessions message each other without a team — lighter than teams for passing findings |

## Quick Reference

| Situation | Default pattern | Why |
|-----------|-----------------|-----|
| 1-2 tasks or shared-file edits | Stay in the main conversation | Parallelism adds coordination overhead without payoff |
| Focused worker that only needs to report back | Claude Code subagent or Codex worker | Isolated context, simple coordination |
| Workers must talk to each other | Claude Code agent team | Shared task list plus direct messaging |
| Read-heavy scans, tests, triage, summarization | Parallel workers | Keeps noisy intermediate output off the lead thread |
| One coordinator should retain user ownership | Manager / agents-as-tools | Lead keeps control of decisions and final answer |
| Specialist should take over the conversation | Handoff | Ownership moves to the specialist agent |
| Work of unknown extent — discovery *is* the task | Loop until K empty rounds | A fixed task list cannot be enumerated up front |
| **Loop Engineering**: recurring discovery or evaluation | Loop-until-dry or budget-bounded loop | Define convergence, termination, and state checkpoints |
| Many items, known stages, high intermediate volume | Scripted workflow (Claude Code) | Script holds control flow; lead context holds only the result |

## Navigation

- [references/loop-orchestration.md](references/loop-orchestration.md) - Bounded iteration vs retry, loop-until-dry, convergence detection, termination predicates, dedup-target rule
- [../ai-coding-agents-tasks/references/loop-and-graph-runtime-surfaces.md](../ai-coding-agents-tasks/references/loop-and-graph-runtime-surfaces.md) - Loop Engineering and Graph Engineering runtime comparison: task queues, cyclic graphs, and workflows
- [references/scripted-workflows.md](references/scripted-workflows.md) - Script-held deterministic control flow (Claude Code Workflows): `agent`/`parallel`/`pipeline`, barrier-vs-pipeline, resume and caching
- [references/platform-patterns.md](references/platform-patterns.md) - Platform guidance for Claude Code subagents, Codex subagents, Codex multi-agents, and OpenAI Agents SDK
- [references/output-contracts.md](references/output-contracts.md) - Task schema, worker report schema, and merge contract
- [references/operational-guardrails.md](references/operational-guardrails.md) - Safety, stop conditions, observability, and verification gates
- [references/cost-discipline.md](references/cost-discipline.md) - Fan-out cost patterns, session lifecycle, loops/schedules audit, orchestration-layer config
- [references/orchestration-maintenance-runbook.md](references/orchestration-maintenance-runbook.md) - How to audit, maintain, and refresh swarm discipline over time
- [references/runtime-smoke-tests.md](references/runtime-smoke-tests.md) - 3 shell-runnable checks for wave dispatch, per-worker budget breach, and wave-boundary checkpoints
- [references/execution-surfaces.md](references/execution-surfaces.md) - Single thread, worker fan-out, agent team, manager, and handoff selection
- [references/noninteractive-and-blueprints.md](references/noninteractive-and-blueprints.md) - CI-safe dispatch patterns and deterministic-plus-agentic blueprint flows
- [references/recipe-wave-dispatch.md](references/recipe-wave-dispatch.md) - Self-contained 3-worker shell example: copy, paste, run, verify
- [references/typical-scenarios.md](references/typical-scenarios.md) - Scenario library: common jobs mapped to surface, pattern, worker shape, and the trap to avoid
- `agents-subagents` - Subagent design, tool scoping, and interruption recovery
- [../agents-hooks/SKILL.md](../agents-hooks/SKILL.md) - Hook guardrails and verification automation
- [../agents-mcp/SKILL.md](../agents-mcp/SKILL.md) - MCP server scoping for workers
- [../agents-skills/SKILL.md](../agents-skills/SKILL.md) - Skill packaging for worker preloads
- [../agents-memory/SKILL.md](../agents-memory/SKILL.md) - Project memory for shared conventions
- [../ai-coding-agents-permissions/SKILL.md](../ai-coding-agents-permissions/SKILL.md) - Approval routing, allow or ask modes, and worker permission handoff
- [../ai-coding-agents-tasks/SKILL.md](../ai-coding-agents-tasks/SKILL.md) - Background task runtimes, teammate queues, and task ownership
- [../dev-workflow-planning/SKILL.md](../dev-workflow-planning/SKILL.md) - Create the plan before fan-out
- [../ai-agents/references/autonomous-loop-patterns.md](../ai-agents/references/autonomous-loop-patterns.md) - Shape C autonomous loops: PRD-driven drivers, circuit breakers, drift detection (framework-neutral)
- [../ai-agents/references/context-graph-patterns.md](../ai-agents/references/context-graph-patterns.md) - Graph-structured agent state: node/edge schema, traversal, conflict resolution
- [data/sources.json](data/sources.json) - Curated official docs, research, and secondary references

> **Maintainer note:** eight URLs here are intentionally duplicated from `../agents-subagents/data/sources.json` (Claude Code subagents, Agent Teams, Codex Multi-Agents, Codex Subagents, both OpenAI Agents SDK pages, OpenAI prompt-caching guide, Karpathy coding notes). Each skill frames those sources for a different reader. When a URL rotates, update both files in the same commit.

## When To Use / Not To Use

| Use | Do Not Use |
|-----|-----------|
| `agents-subagents` already chose the team; now needs execution planning | Still deciding which agent, team, or debate mode fits |
| 3+ bounded tasks with clear ownership or dependencies | Tasks share the same file or unresolved interface |
| Requirements, decisions, synthesis must stay in one lead context | Main blocker is product ambiguity, not execution bandwidth |
| Exploration, tests, logs, or review can run in parallel | Workers would need the same context and make the same decisions |
| Loop Engineering needs a bounded multi-pass orchestration contract | One pass or a simple queue already meets the goal |
| Verification must be explicit, not implied by worker confidence | Work is small enough that orchestration cost exceeds execution cost |

## Relationship To Agents-Subagents

`agents-subagents` is the entry point. It selects the mode and prepares the first launch prompt. This skill takes over when the plan needs multi-wave execution, worker dependencies, verifier passes, or merge/conflict control.

## Operating Principles

- Lead owns requirements, decisions, approvals, and final synthesis — not execution.
- Default to read-heavy parallelism; parallel writes are higher-risk.
- Freeze shared interfaces before dispatching edit-capable workers.
- Give every worker exclusive `owned_files` and explicit `do_not_touch` boundaries.
- Pass distilled dependency outputs, not raw logs or long transcripts.
- Require structured worker reports — the lead validates and merges deterministically.
- Re-plan when conflict resolution costs more than the fan-out saved.
- **Fresh context per worker**: each worker brief contains only its task, plan section, file ownership, and interface contracts — not the lead's full history. Prevents context rot; gives each worker a full window.
- **State in files**: task graph, progress, decisions, and dependency outputs live in structured files (frontmatter MD / JSON / YAML). Any new lead session resumes by reading files, not memory.
- **Checkpoint long runs**: snapshot task state, reports, and decisions to `checkpoints/` at each wave boundary.
- **Budget per worker**: explicit token/time/tool caps at dispatch. Budget-conservation invariant: child budgets are strict subsets of the parent's *remaining* budget. Workers that breach their budget stop and escalate — they do not continue. (Ye & Tan, *Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems*, arXiv:2601.08815, 2026)
- **Telemetry per worker**: assign a run id or span id; log inputs, outputs, status, tokens, and duration to one structured location.
- **Durable approval channels**: route approvals through mailbox/poller with request IDs, not ephemeral callbacks.
- **Minimum toolset per worker**: use `tools`, `disallowedTools`, and `skills` fields to give each worker only what it needs.
- **Memory opt-in**: prefer clean-context workers + file-backed checkpoints. Enable `memory` only when the role genuinely benefits from cross-run priors; never default it for verifiers or reviewers. Prefer file tools over schema-constrained memory APIs. ([Lance Martin, 2026-04-24](https://x.com/RLanceMartin/status/2047720067107033525); [`../ai-context-layer/references/filesystem-as-memory.md`](../ai-context-layer/references/filesystem-as-memory.md))

For context rotation and state handoff patterns, see [`../ai-agents/references/context-rotation-and-state.md`](../ai-agents/references/context-rotation-and-state.md).

## Explicit Fan-Out Is The Durable Default

Claude Opus 4.7 (GA 2026-04-16) shipped a lasting behavior change: it spawns fewer subagents by default than 4.6, favoring single-response completion over implicit parallelism. Fan-out workflows that previously worked without being asked — read-heavy scans, multi-file refactors, review waves, cross-repo audits — now silently serialize unless the lead is told to fan out explicitly. Opus 4.8 (current as of this writing) inherits the same conservative default; treat "assume no auto-parallelism" as the standing assumption for whatever frontier model is current, and re-verify against release notes each time the lead model changes.

Anthropic's source guidance is to give the model **explicit fan-out instructions**; it does not prescribe where the instruction must live. Our repo convention is to install the canonical phrasing once in `AGENTS.md` / `CLAUDE.md` (not duplicated per launch prompt):

> **Spawn multiple subagents in the same turn when fanning out across items or reading multiple files. Do not spawn a subagent for work you can complete in a single response.**

Full guidance and source links live in `agents-subagents`. Judgment call for the lead: after any model swap, run one throwaway fan-out task and watch whether it parallelizes

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
The skill is documentation-heavy and may become stale quickly due to rapidly evolving features (e.g., agent teams are experimental and behavior churns weekly).

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: MIT
Version: 1.0.0
Last GitHub push: Sep 2, 2026
Registry updated: Sep 7, 2026
Instruction path: frameworks/shared-skills/skills/agents-swarm-orchestration/SKILL.md @ 3d5bc7c5b826

Version reported in registry metadata; check source releases before relying on it.

Quality

66/100

Promising

Trust

57/100

Do not auto-install

Audit

74/100

Risky

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "automation",
      "agent-skill"
    ],
    "known_risks": [
      "The skill is documentation-heavy and may become stale quickly due to rapidly evolving features (e.g., agent teams are experimental and behavior churns weekly).",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 86 GitHub stars",
      "Stars/forks activity: 86 stars, 18 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 74,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "The skill is documentation-heavy and may become stale quickly due to rapidly evolving features (e.g., agent teams are experimental and behavior churns weekly).",
      "The SKILL.md references other files that are not fully shown in the excerpt, but they appear to exist in the repository.",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "6d since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "The skill is documentation-heavy and may become stale quickly due to rapidly evolving features (e.g., agent teams are experimental and behavior churns weekly).",
    "No OpenAgentSkill engagement data yet",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing"
  ],
  "agent_contract": {
    "task_input": "Use agents-swarm-orchestration in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 74/100 Risky",
      "Safety: 30/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "vasilyu1983-agents-swarm-orchestration (agents-swarm-orchestration)",
      "install_command": "npx skills add vasilyu1983/AI-Agents-public --skill agents-swarm-orchestration",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "vasilyu1983-agents-swarm-orchestration",
      "task": "Use agents-swarm-orchestration in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/vasilyu1983-agents-swarm-orchestration",
    "api": "https://www.openagentskill.com/api/agent/skills/vasilyu1983-agents-swarm-orchestration",
    "audit": "https://www.openagentskill.com/skills/vasilyu1983-agents-swarm-orchestration/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=vasilyu1983-agents-swarm-orchestration&task=Use%20agents-swarm-orchestration%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20agents-swarm-orchestration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20agents-swarm-orchestration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/vasilyu1983-agents-swarm-orchestration/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/vasilyu1983-agents-swarm-orchestration"
  }
}

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Creator: vasilyu1983
Source: vasilyu1983/AI-Agents-public
Indexed by: OpenAgentSkill community index

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