Imbad0202

Cc User Autopsy — Security skill for Claude Code

Security community

Honest, evidence-traceable peer-review skill for Claude Code users.

How to install Cc User Autopsy

This entry records only its repository, not the path inside it, so there is no exact command to give. Open Imbad0202/cc-user-autopsy and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Cc User Autopsy does

Honest, evidence-traceable peer-review skill for Claude Code users. Rule-based 8-dim scoring + LLM peer review + HTML dashboard. Self-audit and HR-portfolio modes.

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README

cc-user-autopsy

An honest, evidence-traceable peer-review for Claude Code users — a skill that analyzes your own `~/.claude/projects/` transcripts and `~/.claude/usage-data/` session metadata, then generates a standalone HTML report combining rule-based scoring with an LLM-written personalized review.

Built because the built-in `/insights` is useful but leans celebratory; this skill leans diagnostic.

Preview

Two sample outputs, both generated from synthetic data (no real user's information):

`assets/example-output.html` — default self-audit layout (diagnostic letter)
`assets/example-output-hr.html` — hiring-manager / portfolio layout (--audience hr)

To regenerate them locally, see [Running manually](#running-manually).

Three output modes

The same pipeline can produce three variants depending on who the report is for:

Mode	Flag	Audience	Shows project names	Shows session IDs	Shows profile card
Self audit	(default)	You	Verbatim	Verbatim	No
HR / portfolio	`--audience hr` + `--profile` + `--public-projects`	Recruiters	Allowlisted verbatim; rest bucketed	Hidden	Full letterhead
Showcase	`--audience hr` + empty `public_projects: []`	Public readers (blog, demo)	All bucketed into category labels	Hidden	Minimal signature

The skill's Step 0 asks which version(s) to build and walks you through the setup files before running anything. It never silently produces the HR or showcase variant.

What it produces

A standalone HTML file (no remote fonts, no CDN scripts) with:

Overview — session count, tokens, active days, API-equivalent cost, Task agent / MCP adoption, favorite model, cache hit ratio
9-dimension rule-based scores (1–10) with explicit metric evidence:
1. D1 Delegation — Task agent adoption and good-outcome rate
2. D2 Root-cause debugging — iterative_refine