agentleFS
AnonymXXXAnonymXXX/codex-harness-engineering/.agents/skills/harness-engineering/SKILL.md

harness-engineering

Skill3 starsChanged 15 days agoApache-2.0View on GitHub

Choose risk-appropriate execution, worktree isolation, validation, and durable knowledge capture for software implementation and maintenance. Use for feature work, fixes, refactors, configuration, deployment, or explicit Harness/workflow tasks; keep small isolated edits lightweight.

25 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use self-improvement-loops, bdi-mental-states and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • DVwebBiz11Skill

    self-improvement-loops

    This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

    Also in 4 projects that use this

  • Downey6689Skill

    bdi-mental-states

    Transforms external RDF context into BDI mental states. Invoke when modeling beliefs, desires, and intentions.

    Also in 5 projects that use this

  • Downey6689Skill

    hosted-agents

    Guidance for hosted/sandboxed agents. Invoke when running background agents in isolated VMs or multi-client setups.

    Also in 5 projects that use this

  • DVwebBiz11Skill

    long-horizon-prompting

    This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.

    Also in 4 projects that use this

  • AbdelrhmanAlquranSkill

    latent-briefing

    This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

    Also in 4 projects that use this

  • abdullah1854Skill

    context-degradation

    Also in 5 projects that use this

  • +2 more from Downey6689

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 22 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the lightest workflow that completes the user's intended task.

    line 8
  2. Distinctive:

    Read the relevant project rules and inspect repository state.

    line 12
  3. Distinctive:

    Run post-implementation browser acceptance only when explicitly requested or required by a higher-priority instruction.

    line 26
  4. Distinctive:

    Use another skill only for a material task need, not a keyword or quota:

    line 44
  5. Very specific:

    After Harness changes, run uv run ~/.agents/skills/harness-engineering/scripts/harness_doctor.py doctor.

    line 54

“Common” is measured: kept by at least 6 in 10 of the 22 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Harness Engineering

Use the lightest workflow that completes the user's intended task. Follow the global intent and authorization rules; this skill selects execution safeguards, not new permissions.

Choose The Scope

Read the relevant project rules and inspect repository state. Distinguish a request for an answer, review, or diagnosis from a request to change something. Choose from the observed scope:

Prefer the main working directory in every lane. Create a worktree only when the Git workflow's necessity criteria are met; a larger or riskier task alone is not a reason. Reuse an already suitable task checkout when continuing work rather than moving it solely to follow this default.

  • Fast Lane: a clear, cohesive local change in a safe worktree, without shared contracts/configuration, dependencies, generated artifacts, migrations, deployment, data, permissions, or security-sensitive behavior. File count alone does not decide risk.
  • Standard/Heavy Lane: for broader work, read Harness Engineering for proportional validation and conditional documentation capture. The lane alone does not require a plan, worktree, or extra skills.
  • Medium/large Git edits: also read Git Worktree Workflow. Resolve symlink targets before choosing the owning repository. Explain any necessary worktree before creating it; apply bootstrap only to a newly created checkout, and follow the commit and integration rules in either location.

Links above are relative to this repository's .agents/skills/harness-engineering directory; at runtime the same canonical documents are available under ~/.codex/docs/workflows/.

Fast Lane Contract

Inspect relevant code, make the scoped edit in the safe current worktree, and run git diff --check plus the narrowest useful existing lint/test and project-required checks. Add tests only for meaningful behavior or regression risk. Once checks pass, repeat or broaden them only for new changes, failures, or unresolved concerns. Avoid task worktrees, durable documents, plans, full builds, and global cleanup scans unless requested or evidence requires escalation.

Run post-implementation browser acceptance only when explicitly requested or required by a higher-priority instruction; follow the workflow's Browser Acceptance for check selection and UI reporting.

Execution And Boundaries

Carry authorized work through implementation and validation; a proposal or offer to continue is not completion of a change request. Resolve routine choices from evidence and prepare reviewable results before requesting missing authority. Continue independent work while a necessary answer is pending.

A failed check calls for in-scope diagnosis and repair. A blocked operation does not block independent authorized work. Use Stop And Ask only for an unresolved boundary; the global rule owns how to explain a skill-caused pause.

For implementation work, finish with a scoped commit and eligible Local Auto-Merge, unless the user's instructions exclude them.

Before creating or amending a content commit, load git-auto-commit for grouping, staging, and commit-message rules, including routine automatic commits. On an explicit commit request, load it to inspect the requested changes even if the worktree appears clean. The Git workflow owns commit convergence, remote authorization, preflight, and cleanup. Execute separately authorized remote integration; validation alone grants no push or MR/PR permission.

Load the workflow's Capture Gate only for a durable-knowledge candidate or requested documentation work. Use a plan only when resumable state helps. Use subagents under global/runtime rules when a bounded independent task justifies coordination; no delegation quota applies. Keep inter-agent messages readable to humans.

For production tag releases, follow Production Release Completion: finish after pushing and verifying remote refs; wait for production builds or deployment only when explicitly requested by the user.

Specialist Routing

Use another skill only for a material task need, not a keyword or quota:

  • git-auto-commit: a content commit or amendment, or an explicit request to commit; follow its grouping and message rules before Git integration.
  • domain-modeling: unresolved domain language or durable invariants.
  • codebase-design: a non-trivial module/interface or test-seam decision.
  • tdd: explicit test-first work or meaningful behavioral regression protection.
  • diagnosing-bugs: a root cause requiring investigation beyond a localized fix.

Validation And Reporting

After Harness changes, run uv run ~/.agents/skills/harness-engineering/scripts/harness_doctor.py doctor. For skill lifecycle changes or metadata-induced index drift, run its index --write, then index --check. Doctor is read-only; findings do not authorize unrelated repairs. Full/docs checks and periodic cleanup follow the workflow's conditional triggers.

Lead with the outcome, then the evidence and remaining limitations. For rule gardening, identify canonical files and validation. For UI changes, use the workflow's acceptance fields and actual integration state; omit empty boilerplate sections.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.