harness

name	harness
description	Agent engineering harness for any repo. Creates a short AGENTS.md table-of-contents, structured docs/ knowledge base (ARCHITECTURE, QUALITY, CONVENTIONS, COORDINATION, RESILIENCE), custom agent-readable linters (WHAT/FIX/REF format), CI enforcement, and execution plan templates. Supports Rust, Go, TypeScript, and Python. Integrates agent-motivator recovery protocols into docs/RESILIENCE.md (7-point checklist, VBR standards, failure pattern library). Use when setting up any repo for agent-first development, upgrading an existing AGENTS.md, or enforcing architectural lint gates. Includes --audit flag for tool lifecycle checks and L1/L2/L3 progressive disclosure.
license	MIT

harness — Agent Engineering Harness

Implements the OpenAI Codex team's agent-first engineering harness pattern for any repo: short AGENTS.md TOC, structured docs/, custom linters with agent-readable errors, CI enforcement, execution plan templates, doc-gardening.

Validated against: Agent Tool Design Guidelines (2026-03-09)

When to use

• Setting up a new repo for agent-first development
• Upgrading an existing repo's AGENTS.md to table-of-contents style
• Adding architectural lint enforcement to a repo
• Any repo where agents are doing most of the coding

Supported Languages

• Rust (Substrate pallets, cargo workspace)
• Go (internal/ package structure)
• TypeScript (src/, npm)
• Python (pyproject.toml, uv/pytest) ← added 2026-03-09

Usage

SKILL_DIR="$HOME/.openclaw/workspace/skills/harness"

# Scaffold harness for a repo (language auto-detected: Rust/Go/TypeScript/Python)
uv run python "$SKILL_DIR/scripts/scaffold.py" --repo /path/to/repo

# Scaffold with force-overwrite of existing AGENTS.md
uv run python "$SKILL_DIR/scripts/scaffold.py" --repo /path/to/repo --force

# Audit harness freshness (tool lifecycle check — no writes)
uv run python "$SKILL_DIR/scripts/scaffold.py" --repo /path/to/repo --audit

# Run lints locally
bash /path/to/repo/scripts/agent-lint.sh

# Check doc freshness (finds stale references in docs/)
uv run python "$SKILL_DIR/scripts/doc_garden.py" --repo /path/to/repo --dry-run

# Check doc freshness and open a fix PR
uv run python "$SKILL_DIR/scripts/doc_garden.py" --repo /path/to/repo --pr

# Generate execution plan for a complex task
uv run python "$SKILL_DIR/scripts/plan.py" \
  --task "Add IBC timeout handling" \
  --repo /path/to/repo

What gets created

File	Description
AGENTS.md	~100 line TOC with L1/L2/L3 progressive disclosure markers
docs/ARCHITECTURE.md	Layer diagram + dependency rules (auto-generated from repo structure)
docs/QUALITY.md	Coverage targets + security invariants
docs/CONVENTIONS.md	Naming rules (language-specific)
docs/COORDINATION.md	Multi-agent task ownership + conflict resolution rules
docs/RESILIENCE.md	Agent recovery protocols, 7-point checklist, VBR standards ← from agent-motivator
docs/EXECUTION_PLAN_TEMPLATE.md	Structured plan format for complex tasks
scripts/agent-lint.sh	Custom linter with agent-readable errors (WHAT / FIX / REF)
.github/workflows/agent-lint.yml	CI gate on every PR

Lint error format

Every lint error produced by scripts/agent-lint.sh follows this format:

LINT ERROR [<rule-id>]: <description of the problem>
  WHAT: <why this is a problem>
  FIX:  <exact steps to resolve it>
  REF:  <which doc to consult>

This means agents can read lint output and fix problems without asking a human.

Agent Design Checklist (from tool design guidelines)

Before shipping any tool or skill change, verify:

• Is the tool shaped to what this model can actually do?
• Does it schema-enforce structured output where correctness matters?
• Is context loaded progressively (L1→L2→L3), not dumped upfront?
• Does it support multi-agent coordination if needed? (see COORDINATION.md)
• Have you measured model affinity (call frequency) vs just output quality?
• Is the total tool count at or below your ceiling? (target: ≤ 20 per agent)
• Do you have a plan to revisit this tool as model capabilities change?

Progressive Disclosure Layers

The harness enforces a 3-layer context discipline:

Layer	Where	When to load
L1	AGENTS.md	Always — orientation, commands, invariants
L2	docs/	Before coding — architecture, quality, conventions
L3	Source files	On demand — grep/read specific files as needed

Rule: Start with L1. Pull L2 before touching code. Pull L3 only when you need it. Never pre-load all three layers — it crowds out working context.

Tool Lifecycle (--audit)

Run --audit quarterly to check harness freshness:

• AGENTS.md has depth layer markers
• COORDINATION.md present (multi-agent support)
• Lint script uses current language tooling
• Python: ruff + pyright checks present
• AGENTS.md under 150 lines

Safety

• Never overwrites existing AGENTS.md without --force flag
• Reads existing code structure before generating docs (no hallucinated APIs)
• All writes are previewed in --dry-run mode before committing

References

• OpenAI Codex harness engineering
• Agent Tool Design Guidelines
• ClawChain harness PR
• EvoClaw harness PR