Agent skill

Repo Harness Assessment

by hashgraph-online in hashgraph-online/awesome-codex-plugins

A skill your agent uses when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md…

Apache-2.0Auto-check passedAgent Workflows

Install Repo Harness Assessment

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill repo-harness-assessment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins repo-harness-assessment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/yfge/agent-harness-skills/skills/repo-harness-assessment .claude/skills/repo-harness-assessment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
repo-harness-assessment
GitHub stars
1.3k
Token cost
~1.3k tokens
SKILL.md length
592 words
Files
3 (incl. references)
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md…

  • Works in 8 steps: Use rg --files or find to list agent… → Identify one canonical entrypoint and… → Keep the root entrypoint to scope,… → …
  • Evaluating repository agent-readiness
  • SKILL.md covers Overview, When To Use, Inputs Needed and Execution Order, plus 5 more sections
  • Calls rg

What it does

Repo Harness Assessment is an agent skill from hashgraph-online/awesome-codex-plugins. Use when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md, CLAUDE.md, GEMINI.md, Cursor rules, or GitHub instructions.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/build-when-missing.md` and `references/entrypoint-policy.md`).

It sits in Agent Workflows, covering Agent instruction files. It works with GitHub. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Evaluating repository agent-readiness
  • Mapping harness roles
  • Choosing the next smallest improvement
  • Designing and reconciling agent entrypoints such as AGENTS.md

Example prompts

  • “/repo-harness-assessment”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Use rg --files or find to list agent instruction files, docs, scripts, CI, work-state surfaces, ledgers, reports, and runtime artifacts.
  2. Identify one canonical entrypoint and classify every other agent instruction file as a subtree override, symlink, generated mirror, or…
  3. Keep the root entrypoint to scope, source-of-truth navigation, hard boundaries, and minimum commands; move detailed procedures to linked…
  4. Map current artifacts to entrypoint, work-state, ledger, contracts, validation, runtime-evidence, and quality roles before proposing new…
  5. Check for a stable validation matrix and whether failures connect to run IDs, request IDs, logs, screenshots, JSON/JUnit output, reviews…
  6. If a required role is absent, define the minimum bootstrap artifact from references/build-when-missing.md; do not scaffold optional roles…
  7. Add a mirror or pointer drift check when multiple agent instruction surfaces must stay aligned.
  8. Compress gaps into no more than three next steps, ordered by value and risk.

What it can do on your machine

Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • rg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Repo Harness Assessment loads about 1.3k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 592 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 592 words, ~1,303 tokens.

Download SKILL.mdSave it as .claude/skills/repo-harness-assessment/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
repo-harness-assessment
description
Use when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md, CLAUDE.md, GEMINI.md, Cursor rules, or GitHub instructions.

Repo Harness Assessment

Overview

Assess how well a repository lets an agent find its rules, make safe changes, verify them, and produce reviewable evidence.

This is the default router for existing repositories. It also owns agent-entrypoint design because entrypoints are the navigation layer of the assessment, not a separate harness system. For shared vocabulary and neutral artifact names, see ../../references/harness-patterns.md; when expected harness files are missing, use references/build-when-missing.md; for canonical entrypoints, mirrors, and drift prevention, use references/entrypoint-policy.md.

When To Use

  • The user asks what harness pieces a repository is missing.
  • The user wants to create, shrink, reconcile, or diagnose AGENTS.md, CLAUDE.md, GEMINI.md, Cursor rules, or GitHub instructions.
  • You need to compare entrypoints, validation commands, runtime evidence, delivery records, or quality gates across repositories.
  • You need to decide whether the next smallest improvement is an entrypoint, validation script, artifact bundle, ledger, contract check, or quality gate.

Inputs Needed

  • Repository root path.
  • User scope: whole repository, one surface, docs-only work, runtime behavior, CI, or delivery flow.
  • Any expected harness shape or maturity target the user names.
  • Existing agent instruction files and whether mirrors, generation, or subtree overrides are required.

Execution Order

  • First: Read repository entrypoints and source-of-truth files, including agent instructions, README, architecture or reliability docs, indexes, CI, and scripts.
  • Then: Map existing surfaces to harness roles and check entrypoint precedence, mirrors, validation, evidence, work state, delivery, contracts, and quality.
  • Finally: Report maturity, entrypoint actions, the smallest useful improvement slice, and what not to build yet.

Step-by-Step Process

  1. Use rg --files or find to list agent instruction files, docs, scripts, CI, work-state surfaces, ledgers, reports, and runtime artifacts.
  2. Identify one canonical entrypoint and classify every other agent instruction file as a subtree override, symlink, generated mirror, or short pointer.
  3. Keep the root entrypoint to scope, source-of-truth navigation, hard boundaries, and minimum commands; move detailed procedures to linked docs.
  4. Map current artifacts to entrypoint, work-state, ledger, contracts, validation, runtime-evidence, and quality roles before proposing new files.
  5. Check for a stable validation matrix and whether failures connect to run IDs, request IDs, logs, screenshots, JSON/JUnit output, reviews, or commits.
  6. If a required role is absent, define the minimum bootstrap artifact from references/build-when-missing.md; do not scaffold optional roles by default.
  7. Add a mirror or pointer drift check when multiple agent instruction surfaces must stay aligned.
  8. Compress gaps into no more than three next steps, ordered by value and risk.
Show full SKILL.md (190 more words)Show less

Checks

  • Entrypoint: an agent can find first-read docs, edit boundaries, and the minimum validation command within one minute.
  • Consistency: mirrors cannot silently drift, and instruction precedence is explicit.
  • Structure: there is a clear source of truth, directory boundary model, and no-new-drift rule.
  • Validation: there is one local minimum command and one CI gate command.
  • Evidence: a failure can be connected to logs, traces, screenshots, or runtime artifacts.
  • Overbuild: do not recommend a full platform, broad scaffold, or cross-environment deployment system before the minimum slice is justified.

Output Format

markdown
# Repo Harness Assessment

## Detected Mapping
- entrypoint:
- mirrors / subtree overrides:
- work-state:
- ledger:
- contracts:
- validation:
- runtime-evidence:
- quality:

## Entrypoint Decision
- canonical file:
- navigation changes:
- drift prevention:

## Current State
- Agent entrypoint:
- Source-of-truth docs:
- Validation surface:
- Runtime evidence:
- Delivery/ledger:

## Gaps
1.
2.
3.

## Recommended Minimum Slice
- First:
- Then:
- Finally:

## Do Not Build Yet
-

## Validation Needed
-

Common Mistakes

  • Reducing harness maturity to "there are tests."
  • Reading only README while ignoring scripts, CI, and real artifacts.
  • Copying rules into every agent-specific file instead of choosing one canonical source.
  • Turning the root entrypoint into a project encyclopedia.
  • Copying environment variables, ports, accounts, or private workflow details from one implementation into another.
  • Producing a long wish list instead of a small, implementable improvement slice.

Example Prompts

  • "Assess what harness pieces this repository is missing."
  • "Reconcile AGENTS.md, CLAUDE.md, and GEMINI.md without duplicating rules."
  • "Compare this repository against a mature run-artifact pattern."
  • "Can an agent safely take over this project as it stands?"

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in plugins/yfge/agent-harness-skills/skills/repo-harness-assessment of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • references/build-when-missing.md
  • references/entrypoint-policy.md

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Repo Harness Assessment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Repo Harness Assessment compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Repo Harness Assessment this skillhashgraph-online/awesome-codex-plugins1.3k—~1.3kAutomated safety check: PassApache-2.0
AI Self ImprovementJocysCom/FocusLogger213—~1.1kAutomated safety check: PassGPL-3.0
Setup Matt Pocock Skillsywwynm/EverythingDone1448 repos~1.7kAutomated safety check: PassGPL-3.0
AI Readyjohnpapa/ai-ready226—~5.4kAutomated safety check: PassMIT
Arandu Featurearandu-io/arandu281—~2.5kAutomated safety check: PassMIT
Attmcojp Claude Mdnatsukium/dotfiles106—~1.3kAutomated safety check: NotesCC0-1.0

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Works with

Categories

Questions about Repo Harness Assessment

What does Repo Harness Assessment do?

A skill your agent uses when evaluating repository agent-readiness, mapping harness roles, choosing the next smallest improvement, or designing and reconciling agent entrypoints such as AGENTS.md…. Repo Harness Assessment is an agent skill from hashgraph-online/awesome-codex-plugins.md, Cursor rules, or GitHub instructions.

When should I use Repo Harness Assessment?

Repo Harness Assessment fits situations like: evaluating repository agent-readiness; mapping harness roles; choosing the next smallest improvement; designing and reconciling agent entrypoints such as AGENTS.md.

How do I install Repo Harness Assessment in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill repo-harness-assessment -a claude-code`. Or copy the skill folder (plugins/yfge/agent-harness-skills/skills/repo-harness-assessment in hashgraph-online/awesome-codex-plugins) into .claude/skills/repo-harness-assessment in your project. Claude Code loads it when a task matches its description.

How do I install Repo Harness Assessment in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill repo-harness-assessment -a codex`. Or copy the skill folder (plugins/yfge/agent-harness-skills/skills/repo-harness-assessment in hashgraph-online/awesome-codex-plugins) into .agents/skills/repo-harness-assessment in your project. Codex loads it when a task matches its description.

Can I use Repo Harness Assessment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill repo-harness-assessment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/repo-harness-assessment, .gemini/skills/repo-harness-assessment, .github/skills/repo-harness-assessment and .opencode/skills/repo-harness-assessment in your project.

What does Repo Harness Assessment need to run?

Going by SKILL.md and its folder, Repo Harness Assessment needs the command-line tools its instructions call (rg).

Does Repo Harness Assessment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Repo Harness Assessment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Repo Harness Assessment use?

Repo Harness Assessment is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Repo Harness Assessment use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 972 tokens, read only when the agent opens those files.

What are the alternatives to Repo Harness Assessment?

Skills that share tags, products or a category with Repo Harness Assessment: AI Self Improvement (JocysCom/FocusLogger, 213 stars), Setup Matt Pocock Skills (ywwynm/EverythingDone, 144 stars), AI Ready (johnpapa/ai-ready, 226 stars) and Arandu Feature (arandu-io/arandu, 281 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Repo Harness Assessment?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.