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 ← new
`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

Agent Engineering Harness