Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up.

Apache-2.0Auto-check passed

Install Review

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

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins review --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/bcanfield/agentic-tech-debt/skills/review .claude/skills/review && 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
review
GitHub stars
1.3k
Token cost
~963 tokens
SKILL.md length
535 words
Files
2 (incl. scripts)
Skills in repo
716
Repo updated
First seen
Licence
Apache-2.0

At a glance

Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up.

  • The user asks to review debt
  • SKILL.md covers First turn: print the audit, Paydown mode (only on user…, Speak plainly and Don't
  • Runs Python scripts from its folder; calls python3
  • See what to pay down

What it does

Review is an agent skill from hashgraph-online/awesome-codex-plugins. Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up. Use when the user asks to review debt, see what to pay down, work through entries, or invokes $review. Stale entries drop with "drop A,B,C".

Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/review.py`).

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

  • The user asks to review debt
  • See what to pay down
  • Work through entries
  • Invokes $review

Example prompts

  • “drop A,B,C”
  • “/review”

Requirements

  • Python 3

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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    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

Review loads about 963 tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 535 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~69
When it runs · the whole SKILL.md, loaded when a task matches
~963

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); the scripts in this folder 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). 535 words, ~963 tokens.

Download SKILL.mdSave it as .claude/skills/review/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
review
description
Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up. Use when the user asks to review debt, see what to pay down, work through entries, or invokes $review. Stale entries drop with "drop A,B,C".

review — audit + (on follow-up) walk paydown

Two modes. First turn: print the audit and stop. On a user follow-up ("fix the top one," "walk these," "do A," "pay some down"), apply the rubric below.

First turn: print the audit

Run the bundled review.py (it lives in this skill's scripts/ directory — reference it with the relative path; Codex resolves it against the skill root):

bash
python3 scripts/review.py

Optional: --top N to surface more than the default 3 candidates.

Re-emit the helper's stdout verbatim in a fenced code block. Codex may collapse long bash outputs — if you don't print it yourself, the user might not see it. Copy exactly: no preamble, no summary, no "want me to fix the top one?" The fenced block preserves column alignment.

Then stop. The user picks the next move.

Paydown mode (only on user follow-up)

Work through requested entries one at a time. Confirm before each fix. Never auto-batch. Never auto-commit.

For each entry, read the registry file, the hotspot, and adjacent tests. Apply this rubric:

  • Already fixed? If the marker/symptom the entry describes no longer appears in the hotspot file, say so and add the entry's letter to the drop list. Don't re-fix.
  • Cold area? Churn=0 since created: and age >90d → propose deferring. ~20% of files generate ~80% of debt-related rework; don't pay down vanity refactors.
  • Prudent-deliberate with payoff_trigger not met? Honor the trigger. Skip with a one-line "trigger not met: <quote>."
  • Fix candidate? Propose the smallest change that resolves the entry. Improvement, not perfection — don't refactor surrounding code.
When you fix
  • Read the repo first. Check the test framework, adjacent tests, the cached feedback commands. Adapt to what exists; don't impose a new style.
  • TDD where tests exist. Write a failing test that pins the deferral, then make it pass. Don't weaken or delete existing tests to make a fix pass.
  • No tests in this area? Surface that and ask: write one, or fix without?
  • Explain why this resolves the entry. Cite the entry's payoff_trigger or body — don't commit code you can't explain.
  • Risky fix? Auth, payments, migrations, public APIs, or ai_authored: true → run a fresh-context review of the diff before suggesting commit. Fresh-context review catches what the writer's motivated reasoning misses.
  • Don't commit. Show the diff. The user runs the gates, drops the entry with drop A, and commits.
Show full SKILL.md (152 more words)Show less
Pacing

Aim for 3–10 entries per session — continuous paydown outperforms stop-the-world batches. If the user says "do them all," push back once: unsupervised AI cleanup measurably increases duplicate blocks and short-term churn. If they insist, still one-at-a-time with diffs surfaced.

Speak plainly

The frontmatter uses a research taxonomy (quadrant, category) for ranking and grounding — it is not user-facing vocabulary. When you talk about an entry, describe it in plain words; never say "prudent-inadvertent", "reckless-deliberate", "code_rot", etc. to the user. Use the entry's body and a plain phrase (e.g. "a planned tradeoff", "a shortcut you knew about", "came up later") instead. The review.py output is already translated — match its tone.

Don't

  • Don't ask the user to confirm before running review.py.
  • Don't paraphrase the helper's stdout. Copy it verbatim into the fenced code block.
  • Don't enter paydown mode on the first turn. Stop after the report. Wait for the user's intent.
  • Don't auto-commit. Ever.

© 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 1 other file (scripts) in plugins/bcanfield/agentic-tech-debt/skills/review of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • scripts/review.py

Open the folder on GitHubat commit 3e1456a

Compare with similar skills

Review 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.

Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review this skillhashgraph-online/awesome-codex-plugins1.3k—~963Automated safety check: PassApache-2.0
Tech Debt RegisterDonchitos/Claude-Code-Game-Studios26k—~2.4kAutomated safety check: PassMIT
Ponytail Debt LedgerDietrichGebert/ponytail160k—~453Automated safety check: PassMIT
Brooks Debtsickn33/agentic-awesome-skills47k1 repos~542Automated safety check: PassMIT
Tech Debt Auditcode-yeongyu/oh-my-openagent70k—~2.4kAutomated safety check: NotesCustom licence
Code Refactoring Tech Debtsickn33/agentic-awesome-skills47k1 repos~2.9kAutomated safety check: PassMIT

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Questions about Review

What does Review do?

Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up. Review is an agent skill from hashgraph-online/awesome-codex-plugins. Audit the debt registry, rank survivors by churn × Fowler quadrant, surface a top-N list, then walk paydown on user follow-up.

When should I use Review?

Review fits situations like: the user asks to review debt; see what to pay down; work through entries; invokes $review.

How do I install Review in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill review -a claude-code`. Or copy the skill folder (plugins/bcanfield/agentic-tech-debt/skills/review in hashgraph-online/awesome-codex-plugins) into .claude/skills/review in your project. Claude Code loads it when a task matches its description.

How do I install Review in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill review -a codex`. Or copy the skill folder (plugins/bcanfield/agentic-tech-debt/skills/review in hashgraph-online/awesome-codex-plugins) into .agents/skills/review in your project. Codex loads it when a task matches its description.

Can I use Review 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 review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review, .gemini/skills/review, .github/skills/review and .opencode/skills/review in your project.

What does Review need to run?

Going by SKILL.md and its folder, Review needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Review 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 Review 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Review use?

Review 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 Review use?

About 963 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review?

Skills that share tags, products or a category with Review: Tech Debt Register (Donchitos/Claude-Code-Game-Studios, 26k stars), Ponytail Debt Ledger (DietrichGebert/ponytail, 160k stars), Brooks Debt (sickn33/agentic-awesome-skills, 47k stars) and Tech Debt Audit (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review?

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.