Build a complete multi-agent orchestration harness for an OpenClaw (or similar) agentic system — defining a CTO/orchestrator agent, tiered specialist agents,...
Coding
harness
Try itAI harness for stable LLM workflows. Topics — pipeline (clarify → ground → plan → generate → verify, dispatches to code-workflow) [pipeline.md], guardrails (denylist + scope + conditional-reject, self-contained for openclaw headless) [guardrails.md], recovery (fail-analyze → adapt → fallback, self-contained) [recovery.md]. Use when enforcing stable AI agent workflows, applying guardrails to autonomous execution, or recovering from verification failures. "harness", "AI harness", "pipeline guardrails", "fail recovery", "workflow stability", "agent harness" triggers
What it does
AI harness for stable LLM workflows. Topics — pipeline (clarify → ground → plan → generate → verify, dispatches to code-workflow) [pipeline.md], guardrails (denylist + scope + conditional-reject, self-contained for openclaw headless) [guardrails.md], recovery (fail-analyze → adapt → fallback, self-contained) [recovery.md]. Use when enforcing stable AI agent workflows, applying guardrails to autonomous execution, or recovering from verification failures. "harness", "AI harness", "pipeline guardrails", "fail recovery", "workflow stability", "agent harness" triggers
The skill document
Harness
Meta-layer that enforces stable, trustworthy LLM workflows. Composes three concerns into one cohesive harness:
- Pipeline — enforce a
clarify → ground → plan → generate → verifysequence so the agent's output is checked against intent and ground truth before commitment. - Guardrails — block destructive operations, bound work to the user's stated scope, and reject ambiguous instructions with a clarifying question instead of guessing.
- Recovery — when verification fails, analyze the cause, adapt the approach, retry within a bounded budget, then escalate to the human.
Architecture is hybrid: pipeline delegates to code-workflow (DRY — no body duplication), while guardrails and recovery are self-contained so the harness still operates in headless / autonomous runtimes where the full skill set may not be present.
Topic Dispatch
When this skill is invoked with a topic specifier (e.g., /harness pipeline or Skill("harness", "pipeline")), load and follow only the matching topic file (pipeline.md). Do not echo the Topics table or summarize other topics in the response. The Topics table below is an index for invocations without a topic specifier — it is not user-facing output when a topic is named.
Topics
| Topic | Description | Guide |
|---|---|---|
| pipeline | Five-stage workflow gate (clarify → ground → plan → generate → verify) — dispatches to code-workflow for plan/research/implement | pipeline.md |
| guardrails | Pre-execution denylist + scope check + conditional-reject; self-contained for headless runtimes | guardrails.md |
| recovery | Bounded fail-analyze → adapt → retry → fallback; self-contained | recovery.md |
Topic Dependencies
harness (entry — pipeline / guardrails / recovery dispatch)
├─→ pipeline (clarify → ground → plan → generate → verify)
│ └─→ code-workflow/steps (Skill call — plan + research + branch)
│ └─→ code-workflow/implement (Skill call — TDD cycle + build + commit)
├─→ guardrails (self-contained — denylist + scope + conditional-reject)
└─→ recovery (self-contained — fail-analyze + adapt + fallback)
- pipeline: composed flow; calls
code-workflowtopics viaSkill(...)rather than copying their bodies (DRY) - guardrails: self-contained so the harness can guard execution when running detached from the larger skill set (e.g., headless
exec/approvalsruntimes) - recovery: self-contained for the same reason; references
fix(5-Why) andtdd/run(verification-failure handling) patterns by inspiration but does not declare them as runtime dependencies
Quick Reference
Pipeline
clarify → ask for missing intent (no guessing on ambiguous user input)
ground → read source-of-truth (code, docs, API) before planning
plan → produce reviewable plan deliverable
generate → execute the plan
verify → check output against intent + ground truth
The harness owns clarify, ground, and verify orchestration; the plan and generate stages are dispatched via the abstract --pipeline=: contract (defaults to code-workflow:steps and code-workflow:implement).
See detailed guide.
Guardrails
denylist → reject destructive commands without explicit user authorization
scope check → bound work to the user's stated request; refuse drift
conditional reject → on ambiguous instructions, ask instead of guess
Self-contained — no runtime dependency on hook-kit or ask-user. Richer guard machinery can be plugged in via --guard=: when available.
See detailed guide.
Recovery
fail-analyze → identify failure class (input / logic / environment)
adapt → adjust the approach (re-plan / change tool / shrink scope)
retry → bounded attempts (default 2)
fallback → escalate to the user with the failure summary
See detailed guide.
Configuration
| Option | Default | Description |
|---|---|---|
denylist | conservative built-in (destructive shell + git operations) | Command patterns requiring explicit user authorization before execution |
scope-source | {user-request} | Origin of the work-scope boundary used by the guardrails scope check |
retry-budget | 2 | Maximum retries before escalating to fallback |
--pipeline=: | code-workflow:steps + code-workflow:implement | Receiver for the plan / generate stages |
--guard=: | (none — internal denylist) | Optional richer guard machinery |
--recovery=: | (none — internal analyzer) | Optional richer recovery machinery |
All receiver flags follow the abstract dispatch pattern (skill-kit/portability Rule B). Default values keep the harness operational without any external skill installed.
When to Use
- Wrapping an autonomous LLM agent (headless
exec, scripted runs) where you cannot inspect each step yourself - Replaying a workflow that historically drifted into out-of-scope changes
- Establishing a denylist around destructive operations for a coding agent
- Treating verification failure as a recoverable state (analyze + adapt + bounded retry) instead of a hard stop
Related
code-workflow— primary pipeline receiver; carries the plan / generate stagesskill-kit/portability— abstract dispatch contract followed by the receiver options abovefix— recovery topic borrows the 5-Why analysis pattern without runtime dependencytdd/run— recovery references the verification-failure handling pattern
See Also
- Phase 2 (not yet bundled): consolidate + github-flow + web-browser
Related skills
为法律工作者安全生成、增量合并并验证 AGENTS.md/CLAUDE.md
通过 AI-HIVE 完成Agent Harness 工程的规划、路由与验收
Use this skill when designing or operating a Harness-style control layer for OpenClaw setups with many skills, memory surfaces, safety-sensitive tools, playb...
指导AI coding工具构建生产级Agent系统。当用户需要设计、实现或优化AI Agent系统时触发。 特别适用于:Agent架构设计、Harness工程、工具系统、权限模型、上下文管理、多智能体协作、 记忆系统、安全沙箱、MCP集成等场景。支持从概念设计到生产部署的完整Agent系统构建。 触发关键词:构建...
Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define qu...