ccc-makeover

ccc-makeover is a skill for Claude Code from KevinZai/commander. It costs 41 tokens per session (1,388 once invoked), scanned A, original, MIT.

A project-review and improvement skill that scans software across security, testing, delivery operations, code quality, documentation, and performance.

In plain words
What is it for?
Use it to assess project health, produce scorecards and recommendations, apply prioritized fixes, and track changes in the assessment.
Why use it?
It helps identify the most important weaknesses in a project and can apply selected improvements based on the review.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; names the AskUserQuestion tool; mentions Codex.

Good fit Use it to assess project health, produce scorecards and recommendations, apply prioritized fixes, and track changes in the assessment.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kevinzai/commander/ccc-makeover
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add KevinZai/commander --skill ccc-makeover
Clone the repo
git clone --depth 1 https://github.com/KevinZai/commander

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ccc-makeover

README.md
[![agentmods](https://agentmods.dev/badge/skills/kevinzai/commander/ccc-makeover/github.svg)](https://agentmods.dev/skills/kevinzai/commander/ccc-makeover)
Your own site
<a href="https://agentmods.dev/skills/kevinzai/commander/ccc-makeover"><img src="https://agentmods.dev/badge/skills/kevinzai/commander/ccc-makeover/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ccc-makeover

Your own site · 80×15
<a href="https://agentmods.dev/skills/kevinzai/commander/ccc-makeover"><img src="https://agentmods.dev/badge/skills/kevinzai/commander/ccc-makeover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,388 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00041 $0.01388
Opus 5.5 $0.00016 $0.00555
Sonnet 5.5 $0.00008 $0.00278
Haiku 4.5 $0.00004 $0.00139

Measured yesterday against content hash 101ca7142bc7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ccc-makeover scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commander/cowork-plugin-codex/skills/ccc-makeover/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

$ccc-makeover — audit, fix, re-score

CC Commander · $ccc-makeover · X-Ray score, approved fixes in impact order, and a before/after report card.

$ccc-xray owns the scan, its dimensions and its scoring rubric; this skill does not redefine them. The user's selected model is preserved.

Codex: invoke $ccc-xray and ask each picker as a plain-text question.

When to use / when not to

  • Use on an unfamiliar codebase, before a release, after a sprint to measure improvement, or when someone asks "how healthy is this codebase?".
  • Use $ccc-xray alone for a read-only score, $ccc-code-review (or /code-review ultra) for a line-level review, $ccc-design for a visual refresh.

What happens

1. Pick the mode

An argument (xray, makeover, report-card) or a clear intent selects the mode. Otherwise ask with AskUserQuestion (header "Makeover"):

  • 🟢 X-Ray scan — score the project; nothing changes
  • Makeover — scan, then fix the top recommendations one at a time with your OK
  • Report card — current scores, maturity level and history from earlier makeovers

2. X-Ray

Invoke the ccc-xray skill with the Skill tool. Use its scorecard as-is: per-dimension 0–100 scores, its health bands and its fix-first rule.

3. Makeover

  1. Baseline. Invoke ccc-xray with the full scope (or the --category focus). Record the overall score and each dimension score.
  2. Pick targets. Take the critical rows, then the needs-attention rows, ordered by $ccc-xray's fix-first rule (security first, then CI when nothing deploys, then lowest score). Keep at most --max N (default 5). With --category CAT, keep only that dimension.
  3. Dry run. With --dry-run, list the targets with their recommended fix and stop.
  4. Apply, one at a time. For each target ask with AskUserQuestion:
    • 🟢 Apply — run the recommended skill or targeted fix
    • Skip — move to the next target
    • Stop — end the makeover and go to step 6 Skip the question only when the user passed --auto in this request.
  5. Check each fix. After every applied target, run the project's test command (npm test, pnpm test, pytest, go test ./... or cargo test, whichever the repo uses) and git status --short. On a failure, stop and ask:
    • 🟢 Fix it — make a targeted fix and re-run the tests
    • Keep the changes and stop — report the failure as open
    • Revert this target — git restore the files it touched (asks for confirmation first, since it discards those edits)
  6. Re-score. Invoke ccc-xray again with the same scope. Compute the delta per dimension and overall from the two scorecards, not from memory.
  7. Record. Write tasks/makeover-<YYYYMMDD>.md: baseline and final scores, maturity level before and after, each target as applied / skipped / failed, and the test commands run with their results.

Read the full file on GitHub · 103 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday Changed · -1 lines · -28 tokens per session 101ca7142bc7
  2. 14d ago Changed · +27 lines · +22 tokens per session 8f3aaca3b30c
  3. 1mo ago First seen · 77 lines · 47 tokens per session scan A c25b4cf919a5

Subscribe to this mod's changes

ccc-makeover is a skill published in the GitHub repository KevinZai/commander (6 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,388 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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