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Harness Engineering Playbook

  • 314 installs
  • 25 repo stars
  • Updated June 28, 2026
  • broomva/harness-engineering-skill

harness-engineering-playbook is a Claude Code skill that bootstraps OpenAI Harness Engineering artifacts and deterministic agent harness commands for developers running repeated autonomous coding workflows in a repositor

About

harness-engineering-playbook from broomva/harness-engineering-skill operationalizes OpenAI Harness Engineering practices for repositories that agents touch repeatedly. The skill baselines an existing repo, then bootstraps AGENTS.md, PLANS.md, Makefile-style deterministic entrypoints, architecture boundary rules, and entropy-control checks through a Python harness_wizard.py init command with baseline, control, or full profiles. It maps all nine Harness Engineering practices to concrete artifacts, runs harness audit checks that treat missing practices as blocking gaps, and iterates after real agent runs. Shell fallback scripts such as bootstrap_harness.sh scaffold templates when the wizard is unavailable. The repository is migrating to broomva/skills under the same skill name. Developers reach for harness-engineering-playbook when agent runs suffer from setup drift, flaky tests, missing trace IDs, or unclear documentation boundaries.

  • Harness pipeline scaffolding
  • Trigger and stage configuration
  • Cloud and artifact integrations
  • Approval and quality gate patterns
  • Rollback and promotion workflows

Harness Engineering Playbook by the numbers

  • 314 all-time installs (skills.sh)
  • Ranked #327 of 1,435 DevOps & CI/CD skills by installs in the Skillselion catalog
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
npx skills add https://github.com/broomva/harness-engineering-skill --skill harness-engineering-playbook

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Listed on Skillselion
Installs314
repo stars25
Last updatedJune 28, 2026
Repositorybroomva/harness-engineering-skill

How do you bootstrap Harness Engineering for agent repos?

Configure Harness pipelines, triggers, and deployment stages while integrating cloud providers and quality gates into a repeatable delivery workflow.

Who is it for?

Engineering teams adopting OpenAI Harness Engineering patterns who need scripted smoke, test, lint, and entropy controls for autonomous agent runs.

Skip if: Teams looking to configure Harness.io CD pipelines, cloud deployment stages, or one-off scripts without agent-oriented repository guardrails.

When should I use this skill?

A developer asks to bootstrap harness engineering, add AGENTS.md guardrails, or audit a repo for agent-ready deterministic workflows.

What you get

AGENTS.md, PLANS.md, Makefile harness commands, architecture boundary docs, and passing harness audit reports.

  • AGENTS.md
  • PLANS.md
  • deterministic harness Makefile commands

By the numbers

  • Applies 9 OpenAI Harness Engineering practices to repository artifacts
  • Offers 3 bootstrap profiles: baseline, control, and full

Files

.skills/harness-engineering-playbook/SKILL.mdMarkdownGitHub ↗

Harness Engineering Playbook

Use this skill to operationalize the practices from OpenAI's Harness Engineering guide in a repo that agents can run against repeatedly and safely.

What To Load

  • Use references/openai-harness-practices.md for the full practice-to-artifact mapping.
  • Use references/rollout-checklist.md for phased adoption in active repos.
  • Use references/wizard-cli.md for Typer wizard command flows.
  • Use assets/templates/ when creating or updating harness files.

Inputs

  • Target repository path.
  • Existing command surface (make, npm, cargo, pytest, etc.).
  • Existing CI workflows and branch protections.

Workflow

1. Baseline the repo and detect existing workflows. 2. Bootstrap harness artifacts and templates. 3. Apply all nine Harness Engineering practices. 4. Run harness audit checks and repair gaps. 5. Iterate after real agent runs.

Step 1: Baseline The Repo

  • Identify language/toolchain and canonical entrypoints.
  • Inventory existing checks, scripts, and CI jobs.
  • Record current pain points for agent runs: setup drift, unclear docs, flaky tests, missing trace IDs, slow loops.

Use a short baseline note inside PLANS.md so decisions remain durable.

Step 2: Bootstrap Harness Artifacts

Preferred entrypoint:

python3 scripts/harness_wizard.py init <repo-path> --profile control

Profiles:

  • baseline: only core harness artifacts.
  • control: baseline + control-system primitives.
  • full: control + entropy controls (nightly audit + entropy checks).

Direct shell fallback:

Run:

./scripts/bootstrap_harness.sh <repo-path>

This script installs safe defaults from assets/templates/:

  • AGENTS.md
  • PLANS.md
  • docs/ARCHITECTURE.md
  • docs/OBSERVABILITY.md
  • Makefile.harness (+ -include Makefile.harness in Makefile)
  • scripts/audit_harness.sh
  • scripts/harness/{smoke,test,lint,typecheck}.sh
  • .github/workflows/harness.yml

By default, existing files are not overwritten. Pass --force to replace template-managed files.

Step 3: Apply The Nine Practices

Implement each practice directly in repo artifacts.

1. Make Easy To Do Hard Thing

  • Ensure hard, high-value tasks are one command away (make smoke, make check, make ci).
  • Keep setup and cleanup scripted.
  • Make smoke checks cheap enough for frequent use.

2. Communicate Actionable Constraints With Compact Docs

  • Keep AGENTS.md short, concrete, and command-first.
  • Document non-obvious constraints and guardrails.
  • Keep docs close to code and update with behavior changes.

3. Structure Codebase With Strict Boundaries And Flow

  • Define module boundaries in docs/ARCHITECTURE.md.
  • Parse and validate data at boundaries; use typed contracts for internal flow.
  • Prefer one abstraction per module and one clear ownership path.

4. Build Observability In From Day 1

  • Emit structured logs/events with correlation IDs.
  • Capture key transitions in long-running workflows.
  • Define minimum observable fields in docs/OBSERVABILITY.md.

5. Optimize For Agent Flow, Not Human Flow

  • Treat context as a first-class system dependency.
  • Use PLANS.md for multi-step/multi-hour tasks.
  • Front-load durable context (scope, constraints, checkpoints) so restarts stay cheap.

6. Bring Your Own Harness

  • Standardize repo-local wrappers (Makefile.harness, scripts/harness/).
  • Wrap local infra actions in deterministic scripts.
  • Make agent behavior reproducible across machines and runs.

7. Prototype In Natural Language First

  • Draft logic and tests in prose before coding.
  • Review edge cases in prose and lock acceptance criteria.
  • Translate approved prose into code and tests.

8. Invest In Static Analysis And Linting

  • Pin formatter/linter/typechecker versions where practical.
  • Enforce checks in both local workflow and CI.
  • Run static checks before long tests to shorten failure loops.

9. Manage Entropy

  • Add periodic audits for docs drift, flaky checks, and dead scripts.
  • Keep templates synchronized with real workflows.
  • Remove stale abstractions quickly to keep agent context clean.

For a detailed artifact matrix, load references/openai-harness-practices.md.

Step 4: Validate

Run:

python3 scripts/harness_wizard.py audit <repo-path>

Treat any MISSING or FAIL result as blocking before calling harness setup complete.

Step 5: Iterate On Real Runs

  • Observe one full agent run from clean checkout to merged change.
  • Patch harness gaps immediately.
  • Re-run audit.
  • Keep AGENTS.md, PLANS.md, and architecture docs aligned with current behavior.

Adaptation Rules

  • Preserve existing project conventions and replace templates incrementally.
  • Do not overwrite user-authored files without explicit approval.
  • Keep command names stable; change internals behind wrappers.
  • Favor deterministic, scriptable workflows over ad-hoc interactive steps.

Related skills

How it compares

Pick harness-engineering-playbook over generic CI templates when the goal is agent-repeatable repo harnesses rather than cloud pipeline orchestration.

FAQ

Does harness-engineering-playbook configure Harness.io CI/CD?

harness-engineering-playbook implements OpenAI Harness Engineering repository practices, not Harness.io pipeline products. The skill bootstraps AGENTS.md, deterministic command surfaces, architecture boundaries, and entropy audits for autonomous coding agent workflows.

What bootstrap profiles does harness-engineering-playbook offer?

harness-engineering-playbook exposes baseline, control, and full profiles via harness_wizard.py init. Baseline adds core harness artifacts, control adds control-system primitives, and full adds entropy controls including nightly audit and entropy checks.

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