skill-engineer

name	skill-engineer
description	Design, test, review, and maintain agent skills for OpenClaw systems using multi-agent iterative refinement. Orchestrates Designer, Reviewer, and Tester subagents for quality-gated skill development. Use when user asks to "design skill", "review skill", "test skill", "audit skills", "refactor skill", or mentions "agent kit quality".

Skill Engineer

Own the full lifecycle of agent skills in your OpenClaw agent kit. The entire multi-agent workflow depends on skill quality — a weak skill produces weak results across every run.

Core principle: Builders don't evaluate their own work. This skill enforces separation of concerns through a multi-agent architecture where design, review, and testing are performed by independent subagents.

Skill Taxonomy

Source: Anthropic "Improving skill-creator" (2026-03-03)

Skills fall into two categories. This distinction drives design decisions, testing strategy, and lifecycle management.

Capability Uplift Skills

The model can't do it well alone — the skill injects techniques, patterns, or constraints that produce better output than prompting alone.

Examples: Document creation skills (PDF generation), complex formatting, specialized analysis pipelines.

Testing focus: Monitor whether the base model has caught up. If the base model passes your evals without the skill loaded, the skill's techniques have been incorporated into model default behavior. The skill isn't broken — it's no longer necessary.

Lifecycle: These skills may "retire" as models improve. Build evals that can detect when retirement is appropriate.

Encoded Preference Skills

The model can already do each step — the skill sequences operations according to your team's specific process.

Examples: NDA review against set criteria, weekly report generation from specific data sources, brand compliance checks.

Testing focus: Verify the skill faithfully reproduces your actual workflow, not the model's "free improvisation." Fidelity to process is the metric.

Lifecycle: These skills are durable — they encode organizational knowledge that doesn't change with model capability. They need maintenance when processes change, not when models change.

Design Implication

When the Designer begins work, classify the skill:

Classification	Design priority	Test priority	Retirement risk
Capability uplift	Technique precision	Base model comparison	High — monitor model progress
Encoded preference	Process fidelity	Workflow reproduction	Low — tied to org process

Mandatory Dependencies

This skill requires the following to be installed and available:

Dependency	Type	Purpose	Install from
deepwiki	Skill	Query OpenClaw source for current API behavior	liaosvcaf/openclaw-skill-deepwiki
Vector memory DB	OpenClaw feature	Semantic search across session history, notes, and memory files	Enable in openclaw.json (memory.enabled: true)

Before starting any skill design or update session, verify both are available:

# Check deepwiki
ls ~/.openclaw/skills/deepwiki/deepwiki.sh || ls ~/.openclaw/workspace-*/skills/deepwiki/deepwiki.sh

# Check vector memory (should return results, not empty)
# Use the memory_search tool with a known topic from recent sessions

If deepwiki is missing, install from liaosvcaf/openclaw-skill-deepwiki. If vector memory returns no results on known topics, check that memory.enabled is true in openclaw.json and that indexing has run.

Why These Are Non-Negotiable

DeepWiki: OpenClaw APIs are version-specific. Without DeepWiki, skills are written against memory of past behavior — which drifts as OpenClaw updates. DeepWiki grounds skill content in actual source code. A skill engineer without DeepWiki is working blind.

Vector memory DB: Session history, Obsidian notes, and past decisions are indexed here. Without it, the agent falls back to manual file search — slower, less accurate, and misses cross-document connections. Critical context from past sessions (installation guides, design decisions, pitfalls) lives in this index.

Memory Search Protocol (MANDATORY)

Before searching files manually, always query the vector memory database first. It indexes session history, Obsidian notes, and memory files — and finds cross-document connections that manual search misses.

When to query vector memory:

• User asks "do you remember...", "find the notes about...", "we did X before..."
• Looking for past installation guides, design decisions, or troubleshooting records
• Any question about prior work, configurations, or lessons learned

How to query correctly:

memory_search("your query here", maxResults=5)

Critical rule: try multiple queries before giving up.

If the first query returns empty, do NOT fall back to manual file search immediately. Try at least 3 different phrasings:

First query fails	Try instead
"Docker OpenClaw installation"	"dockerized openclaw Titan"
"dockerized openclaw Titan"	"openclaw isolation install guide"
Still empty	Then fall back to manual file search

Lesson learned (2026-03-03): When asked to find Docker/OpenClaw installation notes, memory_search returned empty on the first query and the agent immediately switched to manual SQLite/file search. The correct approach was to try different query phrasings — the second attempt ("dockerized OpenClaw installation Titan setup") returned 5 relevant results directly from indexed Obsidian notes. Manual file search is a last resort, not a first response.

---

DeepWiki Staleness Protocol (MANDATORY)

OpenClaw APIs, skill loading behavior, subagent mechanics, and frontmatter fields are version-specific. Information in this skill or any skill referencing OpenClaw internals may be outdated.

ALWAYS query DeepWiki when:

• Designing a skill that uses sessions_spawn, tool calls, or OpenClaw-specific APIs
• Referencing skill frontmatter fields or loading precedence
• Updating an existing skill that has version-tagged sections
• The installed OpenClaw version differs from any version tag in the skill
• You are unsure whether an API, field, or behavior still exists

How to check:

# Check current OpenClaw version
openclaw --version

# Query DeepWiki for current behavior
~/.openclaw/skills/deepwiki/deepwiki.sh ask openclaw/openclaw "YOUR QUESTION"

Do NOT rely on memory or this skill's documented behavior without verifying when the topic is OpenClaw internals. DeepWiki is grounded in the actual source code. This skill's documentation is not.

Verification checklist before shipping any skill that references OpenClaw internals:

• Checked openclaw --version against version tags in the skill
• Queried DeepWiki to confirm API/field behavior is current
• Updated version tags if behavior has changed

---

Scope & Boundaries

What This Skill Handles

• Skill design: SKILL.md, skill.yml, README.md, tests, scripts, references
• Skill review: quality evaluation, rubric scoring, gap analysis
• Skill testing: self-play validation, trigger testing, functional testing
• Skill maintenance: iteration based on feedback, refactoring
• Agent kit audit: inventory, consistency, quality scoring across all skills

What This Skill Does NOT Handle

• Release pipeline — publishing, versioning, changelogs belong to release processes
• Repository management — git submodules, repo creation, branch strategy belong to your VCS workflow
• Deployment — installing skills to agents, configuration management
• Tracking — progress tracking, task management, project boards
• Infrastructure — MCP servers, API keys, environment setup

Where This Skill Ends

This skill produces validated skill artifacts (SKILL.md, skill.yml, README.md, tests, scripts). Once artifacts pass quality gates, responsibility transfers to whatever system handles publishing and deployment.

Success Criteria

A skill development cycle is considered successful when:

1. Quality gates passed — Reviewer scores ≥28/33 (Deploy threshold)
2. No blocking issues — Tester reports no issues marked as "blocking"
3. All artifacts generated — SKILL.md, skill.yml, README.md, tests/, scripts/ (if needed), references/ (if needed)
4. OPSEC clean — No hardcoded secrets, paths, org names, or private URLs
5. Scripts validated — All deterministic validation scripts execute successfully on target platform(s)
6. Trigger accuracy — Tester reports ≥90% trigger accuracy (true positives + true negatives)

If any criterion fails, the skill returns to the Designer for revision.

Inputs

When invoking this skill, the orchestrator must gather:

Input	Description	Required	Source
Problem description	What capability or workflow needs to be enabled	Yes	User conversation
Target audience	Which agent(s) will use this skill	Yes	User or inferred
Expected interactions	With users, APIs, files, MCP servers, other skills	Yes	Requirements discussion
Inputs/Outputs	What data the skill receives and produces	Yes	Requirements discussion
Constraints	Performance limits, security requirements, dependencies	No	User or system
Prior feedback	Review or test reports from previous iterations	No	Previous Reviewer/Tester
Existing artifacts	If refactoring/maintaining an existing skill	No	File system

Example requirements gathering:

User: "I need a skill for analyzing competitor websites"

Orchestrator gathers:
- Problem: Automate competitor analysis with structured output
- Audience: research-agent
- Interactions: web_fetch, browser tool, writes markdown reports
- Inputs: competitor URLs, analysis criteria
- Outputs: comparison table, insights markdown
- Constraints: must complete in <60s per site

These inputs are then passed to the Designer to begin the design process.

---

Architecture Overview

The skill-engineer uses a three-role iterative architecture. The orchestrator spawns subagents for each role and never does creative or evaluation work directly.

Pattern Selection (IMPORTANT)

Two architecture modes are available. Choose based on complexity:

Mode A: Director-Controlled (simple/short skill work) Use when: ≤2 phases, <10 minutes total, user interaction needed between phases (e.g., quick fixes, single-skill reviews).

Director/Orchestrator (main agent, depth 0)
    ├─ Spawn ──→ Designer (depth 1)
    ├─ Spawn ──→ Reviewer (depth 1)
    └─ Spawn ──→ Tester (depth 1)

Risk: announce-to-action gap — if user sends a message while waiting for a subagent, the main agent may handle that instead of chaining the next phase. Mitigate with cron safety net (see below).

Mode B: Orchestrator Subagent Pattern (complex/long skill work) Use when: 3+ phases, >10 minutes, pipeline must not stall, parallel workers needed.

Director (user-facing, depth 0)
    └── Orchestrator (pipeline owner, depth 1)
        ├─ Spawn ──→ Designer (depth 2)
        ├─ Spawn ──→ Reviewer (depth 2)
        └─ Spawn ──→ Tester (depth 2)

The Director spawns a single Orchestrator subagent with the full task description. The Orchestrator owns the entire Design→Review→Test loop without yielding control between phases. User messages go to the Director; the pipeline runs uninterrupted.

Required config for Mode B:

{
  "agents": { "defaults": { "subagents": { "maxSpawnDepth": 2 } } }
}

Why Mode B is superior for complex work:

• No announce-to-action gap (orchestrator chains phases immediately within the same session)
• Immune to user interruption between phases
• Persistent pipeline state without re-deriving from files each turn

Reference: orchestrator-subagent-pattern-2026-02-28.md (Obsidian notes) — documented after a real 70-minute pipeline stall incident.

Mode A Safety Net (cron)

When using Mode A, set a cron safety net after each spawn to catch announce-to-action failures:

"Check if [designer/reviewer/tester] subagent has completed. If so and next phase not started, resume pipeline."
(fires 15 min after spawn)

Iteration Loop

Designer → Reviewer ──pass──→ Tester ──pass──→ Ship
              │                  │
              fail               fail
              │                  │
              ▼                  ▼
         Designer revises   Designer revises
              │                  │
              ▼                  ▼
           Reviewer          Reviewer + Tester
              │
           (max 3 iterations, then fail)

Exit conditions:

• Ship: Reviewer scores ≥ 28/33 (85%+) AND Tester reports no blocking issues
• Revise: Reviewer or Tester found fixable issues (iterate)
• Fail: 3 iterations exhausted and still below quality bar

Iteration Failure Path

After 3 failed iterations, the orchestrator must:

1. Stop iteration — do not continue trying
2. Report failure to user with:
   • Summary: "Skill development failed after 3 iterations"
   • All 3 iteration reports (Reviewer + Tester feedback)
   • Final quality score
   • List of unresolved blocking issues
3. Present options to user:
   • Provide more context or clarify requirements (restart with better inputs)
   • Simplify scope (reduce skill complexity and retry)
   • Abandon this skill (requirements may be infeasible)
4. Do NOT silently fail — always report to user and await decision

Never: Continue past 3 iterations or ship a skill that hasn't passed quality gates.

Subagent Spawning Mechanism

Version note: Verified against OpenClaw v2026.2.26. API may change.

In OpenClaw, subagents are spawned using the sessions_spawn tool (not a CLI command). Subagents run in isolated sessions, announce results back to the requester's channel when complete, and are auto-archived after 60 minutes by default.

Key constraints on subagents:

• Default max spawn depth is 1 (subagents cannot spawn further subagents unless maxSpawnDepth: 2 is configured)
• Default max 5 active children per agent at once
• Subagents do NOT receive SOUL.md, IDENTITY.md, or USER.md — only AGENTS.md and TOOLS.md
• Use runTimeoutSeconds to prevent hanging (900s for Designer, 600s for Reviewer/Tester)
• Results are announced back automatically; reply ANNOUNCE_SKIP to suppress

---

Director vs. Orchestrator Roles

This is the most important architectural decision. Understand it before proceeding.

The Problem with Naive Single-Agent Control

The natural instinct is to have the main agent (you) directly manage the Design→Review→Test loop:

Main agent
    ├── spawns Designer → waits for announce → spawns Reviewer → waits → spawns Tester

This breaks in three ways:

1. Announce-to-action gap: When a subagent finishes, OpenClaw sends a completion announce that triggers a new LLM turn. The LLM may report results to the user and stop — treating the announce as informational rather than a pipeline trigger. There is no mechanism that forces the next action.

2. Context loss: Each new turn is a fresh LLM call. Between subagent completion and the next turn, there is no persistent state machine tracking "we're in iteration 2, reviewer passed, now run Tester." The agent must re-derive this from files every time — fragile over 3+ iterations.

3. User message interruption: If the user sends a message while the pipeline is between phases, the main agent handles that message instead of continuing. The pipeline stalls silently until the user notices.

Real incident: A book-writer pipeline stalled for 70 minutes because a research subagent completed and announced back, but the Director reported results to the user and stopped — never spawning the writing phase. (2026-02-28)

The Solution: One Level of Indirection

Add an intermediate Orchestrator subagent that owns the pipeline. The main agent becomes the Director — it talks to the user. The Orchestrator does the pipeline work. They don't share context.

Director (main agent, depth 0)  ←→  User
    │
    └── Orchestrator (subagent, depth 1) — owns Design→Review→Test loop
        ├── Designer (depth 2)
        ├── Reviewer (depth 2)
        └── Tester (depth 2)

Why this works:

• The Orchestrator runs as a single continuous session. It processes each subagent's completion announce immediately — no turn boundary between phases, no gap.
• User messages go to the Director (depth 0), not the Orchestrator. The pipeline cannot be interrupted by user activity.
• The Orchestrator maintains full pipeline state throughout its run without re-deriving from files.

Required config (add to openclaw.json before using this pattern):

{
  "agents": { "defaults": { "subagents": { "maxSpawnDepth": 2 } } }
}

When to Use Each Mode

Situation	Use	Why
Quick fix, single skill review, <10 min	Director-only (depth 1 subagents)	Simpler, fewer spawns
Full design cycle (Design+Review+Test)	Director + Orchestrator (depth 2)	Pipeline cannot afford to stall
Any pipeline with 3+ sequential phases	Director + Orchestrator (depth 2)	Announce-to-action gap becomes critical
maxSpawnDepth not set to 2	Director-only with cron safety net	No choice — see fallback below

Fallback: Director-Only with Cron Safety Net

If maxSpawnDepth: 2 is not configured, use Director-only mode but add a cron safety net after each subagent spawn:

After spawning Designer, register a cron job:
"Check if Designer has completed (look for output at /path/to/skill/SKILL.md).
 If completed and Reviewer not yet started, spawn Reviewer now."
(fires 15 minutes after spawn)

This mitigates but does not eliminate the announce-to-action gap.

---

Director Responsibilities

The Director (main agent) talks to the user and kicks off the pipeline. It does NOT do design, review, or testing work.

1. Gather requirements from the user (problem, audience, inputs/outputs, interactions)
2. Query DeepWiki — if the skill touches any OpenClaw internals, query DeepWiki FIRST:

   ~/.openclaw/skills/deepwiki/deepwiki.sh ask openclaw/openclaw "RELEVANT QUESTION"
   

3. Choose mode — Director-only (simple) or Director+Orchestrator (full cycle)
4. For Director+Orchestrator mode: Spawn a single Orchestrator subagent with complete task description including: requirements, DeepWiki findings, artifact output path, quality rubric location, max iterations
5. For Director-only mode: Execute Orchestrator Responsibilities directly (see below)
6. Relay final result to user when pipeline completes

Orchestrator Responsibilities

The Orchestrator (depth-1 subagent in Mode B, or main agent in fallback mode) owns the Design→Review→Test loop. It does NOT write skill content or evaluate quality — it only coordinates.

1. Query DeepWiki for any OpenClaw-specific topics in the requirements (if Director hasn't already)
2. Spawn Designer with requirements, DeepWiki findings, and any prior feedback

   sessions_spawn(
     task="Act as Designer. Requirements: [...]. Write artifacts to /path/to/skill/",
     label="skill-v1-designer",
     runTimeoutSeconds=900
   )
   

3. Collect Designer output — verify all required files exist at output path
4. Spawn Reviewer with artifacts and quality rubric

   sessions_spawn(
     task="Act as Reviewer. Evaluate skill at /path/to/skill/ using rubric: [...]. Score all 33 checks.",
     label="skill-v1-reviewer",
     runTimeoutSeconds=600
   )
   

5. Collect Reviewer feedback (scores + structured issues)
6. If score <28/33 or blocking issues: feed feedback back to Designer → go to step 2, increment iteration count
7. If passing review: Spawn Tester

   sessions_spawn(
     task="Act as Tester. Run self-play on skill at /path/to/skill/. Test triggers, functional steps, edge cases.",
     label="skill-v1-tester",
     runTimeoutSeconds=600
   )
   

8. Collect Tester results (pass/fail + report)
9. If blocking issues: feed test results back to Designer → go to step 2
10. If all pass: add quality scorecard to README.md → announce completion to Director
11. Track iteration count — after 3 failed iterations, report failure with all iteration logs

Final Review Scores in README

Every shipped skill must include a quality scorecard in its README.md. This is the Reviewer's final scores, added by the Orchestrator before delivery:

## Quality Scorecard

| Category | Score | Details |
|----------|-------|---------|
| Completeness (SQ-A) | 7/7 | All checks pass |
| Clarity (SQ-B) | 4/5 | Minor ambiguity in edge case handling |
| Balance (SQ-C) | 4/4 | AI/script split appropriate |
| Integration (SQ-D) | 4/4 | Compatible with standard agent kit |
| Scope (SCOPE) | 3/3 | Clean boundaries, no leaks |
| OPSEC | 2/2 | No violations |
| References (REF) | 3/3 | All sources cited |
| Architecture (ARCH) | 2/2 | Separation of concerns maintained |
| **Total** | **29/30** | |

*Scored by skill-engineer Reviewer (iteration 2)*

This scorecard serves as a quality certificate. Users can assess skill quality before installing.

Version Control

The orchestrator manages git commits throughout the workflow:

When to commit:

• After Designer produces initial artifacts (iteration 1): git add . && git commit -m "feat: initial design for <skill-name>"
• After Designer revisions (iteration 2+): git add . && git commit -m "fix: address review issues (iteration N)"
• After Tester passes and before ship: git add README.md && git commit -m "docs: add quality scorecard for <skill-name>"

When to push:

• After final ship (all gates passed): git push origin main
• Do NOT push intermediate iterations — only ship-ready artifacts

Branch strategy:

• Work in main branch for routine skill development
• Use feature branches for experimental or breaking changes

Error Handling

The orchestrator must handle technical failures gracefully:

Failure Type	Detection	Response
Git push fails	Exit code ≠ 0	Retry once. If fails again, report to user: "Cannot push to remote. Check network/permissions."
OPSEC scan script missing	File not found	Skip OPSEC automated check, but flag in review: "Manual OPSEC review required — script not found."
File write errors	Permission denied	Report: "Cannot write to [path]. Check file permissions." Fail workflow.
Subagent crashes	Timeout or error	Log the error, attempt retry once. If fails again, report: "Subagent failed. Manual intervention required."
Review score = 0	All checks fail	Report: "Skill failed all quality checks. Requirements may be unclear or skill design is fundamentally flawed. Recommend starting over."

Retry logic:

• Git operations: 1 retry after 5s delay
• File operations: 1 retry after 2s delay
• Subagent spawns: 1 retry with fresh context

Fail-fast rules:

• If iteration count exceeds 3, fail immediately (no further retries)
• If OPSEC violations found, fail immediately (no iteration)
• If required files cannot be written, fail immediately

Performance Notes

Orchestrator workload: Coordinating Designer/Reviewer/Tester across 1-3 iterations can be complex, especially for large skills (1000+ lines). The orchestrator manages:

• Requirements gathering
• Subagent coordination (3-9 spawns in typical workflow)
• Feedback routing between roles
• Iteration tracking
• Final scorecard assembly
• Git operations

Token considerations: A full 3-iteration cycle can consume 50k-150k tokens depending on skill complexity. For extremely complex skills, consider:

• Breaking into sub-skills (each with simpler scope)
• Using separate agent sessions (Option 2 spawning) to isolate token contexts
• Simplifying requirements before starting iteration

If orchestrator feels overwhelmed: This is a signal that the skill being designed may be too complex. Revisit the scope definition and consider decomposition.

Spawning Context

Each subagent receives only what it needs:

Role	Receives	Does NOT Receive
Designer	Requirements, prior feedback (if any), design principles	Reviewer rubric internals
Reviewer	Skill artifacts, quality rubric, scope boundaries	Requirements discussion
Tester	Skill artifacts, test protocol	Review scores

---

Designer Role

Purpose: Generate and revise skill content.

For complete Designer instructions, see: references/designer-guide.md

Quick Reference

Inputs: Requirements, design principles, feedback (on iterations 2+)

Outputs: SKILL.md, skill.yml, README.md, tests/, scripts/, references/

Key constraints:

• Apply progressive disclosure (frontmatter → body → linked files)
• Apply scoping rules (explicit boundaries, no scope creep)
• Apply tool selection guardrails (validate before execution)
• README for strangers only (no internal org details)
• Follow AI vs. Script decision framework

Design principles:

• Progressive disclosure (3-level system)
• Composability (works alongside other skills)
• Portability (same skill works across Claude.ai, Claude Code, API)

---

Reviewer Role

Purpose: Independent quality evaluation. The Reviewer has never seen the requirements discussion — it evaluates artifacts on their own merits.

For complete Reviewer rubric and scoring guide, see: references/reviewer-rubric.md

Quick Reference

Inputs: Skill artifacts, quality rubric, scope boundaries

Outputs: Review report with scores, verdict (PASS/REVISE/FAIL), issues, strengths

Quality rubric (33 checks total):

• SQ-A: Completeness (8 checks)
• SQ-B: Clarity (5 checks)
• SQ-C: Balance (5 checks)
• SQ-D: Integration (5 checks)
• SCOPE: Boundaries (3 checks)
• OPSEC: Security (2 checks)
• REF: References (3 checks)
• ARCH: Architecture (2 checks)

Scoring thresholds:

• 28-33 pass → Deploy (PASS verdict)
• 20-27 pass → Revise (fixable issues)
• 10-19 pass → Redesign (major rework)
• 0-9 pass → Reject (fundamentally flawed)

Pre-review: Run deterministic validation scripts before manual evaluation

---

Tester Role

Purpose: Empirical validation via self-play. The Tester loads the skill and attempts realistic tasks.

For complete Tester protocol, see: references/tester-protocol.md

Quick Reference

Inputs: Skill artifacts, test protocol

Outputs: Test report with trigger accuracy, functional test results, edge cases, blocking/non-blocking issues, verdict (PASS/FAIL)

Test protocol:

1. Trigger tests — verify skill loads correctly (≥90% accuracy threshold)
2. Functional tests — execute 2-3 realistic tasks, note confusion points
3. Edge case tests — missing inputs, ambiguous requirements, boundary cases

Issue classification:

• Blocking: Prevents skill from functioning (must fix before ship)
• Non-blocking: Impacts quality but doesn't break core functionality

Pass criteria: No blocking issues + ≥90% trigger accuracy

Separation of Concerns Rule

The agent that DESIGNS a skill must NOT be the same agent that AUDITS it in the same session.

This is a hard architectural rule, not a guideline. When the same agent designs and audits in one session, it creates structural circularity: the designer unconsciously frames evaluation in terms of their own intentions, missing gaps that a fresh reader would catch.

Enforcement:

• All audit work (Reviewer role, Tester role) MUST be performed by a fresh subagent spawned after design is complete.
• Use openclaw agent --session-id <unique-id> (Option 2 spawning) when auditing a skill the current session has designed.
• The orchestrator may never evaluate its own spawned Designer's output directly — it must route all evaluation through an independent Reviewer subagent.
• In role-based execution (Option 1), the agent must explicitly transition: complete all Designer work, then start the Reviewer role with no reference to design-time reasoning.

Why this matters:

• A designer who audits their own work will score it against their intentions, not against what a new agent will actually experience.
• The rubric (SQ-C3) explicitly prohibits a sub-agent from being both producer AND evaluator of the same output.
• This rule is the implementation of that check at the session level.

Example — correct:

# Session A: Designer work
sessions_spawn(
  task="Design a skill for X. Write artifacts to /path/to/skill/",
  label="skill-v1-designer",
  runTimeoutSeconds=900
)

# Session B: Audit (fresh session, no context from Session A)
sessions_spawn(
  task="Audit the skill at /path/to/skill/ using the reviewer rubric.",
  label="skill-v1-auditor",
  runTimeoutSeconds=600
)

Example — incorrect:

[Session A]
1. Design the skill...
2. Now let me review the skill I just designed...  ← VIOLATION

---

---

Evals Framework

Source: Anthropic "Improving skill-creator" (2026-03-03). Adapted for OpenClaw skill-engineer.

Evals turn "seems to work" into "verified to work." Every shipped skill should have persistent evals that can be re-run after model updates, skill edits, or environment changes.

Eval Structure

An eval consists of:

1. Test prompt — a realistic user input that should trigger the skill
2. Expected behavior description — what "good" looks like (natural language, not exact match)
3. Pass/fail criteria — specific, observable conditions

Store evals in the skill's tests/ directory:

tests/
├── evals.json           # Eval definitions
├── benchmarks/          # Benchmark run results (timestamped)
└── comparisons/         # A/B comparison results

Eval Types

Type	Purpose	When to run
Regression eval	Catch quality drops after changes	After every skill edit or model update
Capability eval	Detect if base model has outgrown the skill	Monthly, or after major model releases
Trigger eval	Verify skill fires correctly	After description changes

Benchmark Mode

Run standardized assessments tracking:

• Eval pass rate — what percentage of evals pass
• Elapsed time — how long each eval takes
• Token usage — cost per eval run

Store benchmark results with timestamps for trend tracking:

{
  "timestamp": "2026-03-04T12:00:00Z",
  "skill": "my-skill",
  "model": "claude-sonnet-4-5",
  "pass_rate": 0.85,
  "avg_time_s": 12.3,
  "avg_tokens": 4200,
  "evals_run": 10
}

Comparator Testing (A/B Blind Test)

Compare two skill versions — or skill vs. no skill — using a blind judge:

1. Run the same test prompt through Version A and Version B
2. A Comparator subagent (fresh context, no knowledge of which is which) evaluates both outputs
3. The Comparator scores on relevant dimensions without knowing the source

When to use:

• Before shipping a major revision (old vs. new)
• To justify a skill's existence (with-skill vs. without-skill)
• To compare two alternative approaches during design

Spawning a Comparator:

sessions_spawn(
  task="You are a blind comparator. You will receive Output A and Output B for the same task. Score each on [dimensions]. You do NOT know which version produced which output. Be objective.",
  label="skill-comparator",
  runTimeoutSeconds=300
)

Description Tuning

Skill descriptions determine trigger accuracy. As skill count grows, description precision becomes critical:

• Too broad → false triggers (skill loads when irrelevant)
• Too narrow → misses (skill doesn't load when needed)

Tuning protocol:

1. Collect 10-20 sample prompts (mix of should-trigger and should-not-trigger)
2. Run each prompt and check whether the skill triggers correctly
3. Analyze false positives and false negatives
4. Revise the description field to be more precise
5. Re-run trigger tests to verify improvement

Target: ≥90% trigger accuracy on sample prompts. Anthropic's internal testing improved 5 out of 6 public skills using this method.

Skill Retirement Protocol

Skills are not forever. Capability uplift skills may become unnecessary as models improve.

Retirement signal: Base model passes ≥80% of the skill's evals without the skill loaded.

Retirement process:

1. Run capability evals with skill disabled
2. If pass rate ≥80%, flag skill as "retirement candidate"
3. Run comparator test (with-skill vs. without-skill) to confirm
4. If comparator shows no significant quality difference, retire the skill
5. Archive (don't delete) — the skill may become relevant again with different models

Track in audit reports:

## Retirement Candidates

| Skill | Capability Eval (no skill) | Comparator Result | Recommendation |
|-------|---------------------------|-------------------|----------------|
| pdf-creator | 85% pass | No significant difference | Retire |

Agent Kit Audit Protocol

Periodic full audit of the agent kit:

1. Inventory all skills — list every SKILL.md with owner agent
2. Check for orphans — skills that no agent uses
3. Check for duplicates — overlapping functionality
4. Check for gaps — workflow steps that have no skill
5. Check balance — are some agents overloaded while others idle?
6. Check consistency — naming conventions, output formats
7. Run quality score on each skill (SQ-A through SQ-D)
8. Produce audit report with scores and recommendations

Audit Output Template

# Agent Kit Audit Report

**Date:** [date]
**Skills audited:** [count]

## Skill Inventory

| # | Skill | Agent | Quality Score | Status |
|---|-------|-------|--------------|--------|
| 1 | [name] | [agent] | X/33 | Deploy/Revise/Redesign |

## Issues Found
1. ...

## Recommendations
1. ...

## Action Items
| # | Action | Priority | Owner |
|---|--------|----------|-------|

---

Skill Interaction Map

Maintain a map of how skills interact:

orchestrator-agent (coordinates workflow)
    ├── content-creator (writes content)
    │   └── consumes: research outputs, review feedback
    ├── content-reviewer (reviews content)
    │   └── produces: review reports
    ├── research-analyst (researches topics)
    │   └── produces: research consumed by content-creator
    ├── validator (validates outputs)
    └── skill-engineer (this skill — meta)
        └── consumes: all skills for audit

Adapt this to your specific agent architecture.

---

OpenClaw Skill System Reference

Version note: This section is based on OpenClaw v2026.2.26. Skill system behavior (frontmatter fields, loading precedence, subagent APIs) may change across versions. Verify against source or DeepWiki when upgrading.

Skill Structure

A skill is a directory containing at minimum a SKILL.md file:

my-skill/
├── SKILL.md          # Required: frontmatter + instructions
├── skill.yml         # Optional: ClawhHub publish metadata
├── README.md         # Optional: human-facing documentation
├── scripts/          # Optional: deterministic helper scripts
├── tests/            # Optional: test cases and fixtures
└── references/       # Optional: detailed linked documentation

SKILL.md Frontmatter Format

Required fields:

---
name: skill-name          # kebab-case, no spaces/capitals/underscores
description: |            # What it does + when to use it + trigger phrases
  [What it does]. Use when user [trigger phrases]. [Key capabilities].
---

Full supported fields:

---
name: skill-name
description: ...
homepage: https://...                    # URL for Skills UI
user-invocable: true                     # Expose as slash command (default: true)
disable-model-invocation: false          # Exclude from model prompt (default: false)
command-dispatch: tool                   # Bypass model, dispatch to tool directly
command-tool: tool-name                  # Tool to invoke when command-dispatch is set
command-arg-mode: raw                    # Argument forwarding mode (default: raw)
metadata: {"openclaw": {"always": true, "emoji": "🔧", "os": ["darwin","linux"], "requires": {"bins": ["curl","python3"]}, "primaryEnv": "MY_API_KEY"}}
---

metadata.openclaw load-time gates:

Field	Purpose
always: true	Always include, skip all other gates
emoji	Emoji shown in macOS Skills UI
os	Limit to platforms: darwin, linux, win32
requires.bins	All binaries must exist on PATH
requires.anyBins	At least one binary must exist
requires.env	Environment variables must exist
requires.config	openclaw.json paths must be truthy
primaryEnv	Links to skills.entries.<name>.apiKey in config

Skill Loading Precedence

Skills are loaded from these locations (highest → lowest priority):

1. <workspace>/skills/ — agent-specific, highest precedence
2. ~/.openclaw/skills/ — shared across all agents on machine
3. skills.load.extraDirs in openclaw.json — additional directories
4. Bundled skills — shipped with OpenClaw, lowest precedence
5. Plugin skills — listed in openclaw.plugin.json

When to Use Each Location

Location	Use when
<workspace>/skills/	Skill is specific to one agent's role; under active development
~/.openclaw/skills/	Skill should be available to all agents on this machine

How Skills Are Triggered

OpenClaw builds a system prompt with a compact XML list of available skills (name, description, location). The model reads this list and decides which skills to load. Skills are NOT auto-injected — the model must explicitly read the SKILL.md when needed.

Trigger accuracy goal: ≥90% (skill loads when relevant, does NOT load when irrelevant).

Skill Discovery Command

To inventory all skills on a machine:

find ~/.openclaw/ -name "SKILL.md" -not -path "*/node_modules/*" | sort

Configuration

No persistent configuration required. The skill uses tools available in the agent's environment.

Requirement	Description
deepwiki skill	Query OpenClaw source for current API behavior (liaosvcaf/openclaw-skill-deepwiki)
Vector memory	Semantic search across session history (memory.enabled: true in openclaw.json)
gh CLI	GitHub repo creation and visibility changes for release pipeline
clawhub CLI	Publish skills to ClawhHub registry (npm i -g clawhub)

See references/designer-guide.md for full environment setup.