roast-my-computer

roast-my-computer is a skill for Claude Code, Codex from thedavidweng/skills. It costs 78 tokens per session (1,880 once invoked), scanned A, original, MIT.

A local report generator that scans a developer's computer and presents findings about clutter, abandoned projects, Git habits, unused tools, and possible secret leaks.

In plain words
What is it for?
It reviews developer folders, downloads, desktop files, source code, dotfiles, credentials, dependencies, and old reports, then creates an HTML report.
Why use it?
It helps reveal maintenance and privacy risks spread across folders and projects, while keeping the scan data and report on the computer.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the david-skills plugin — 22 skills shipped together

Good fit It reviews developer folders, downloads, desktop files, source code, dotfiles, credentials, dependencies, and old reports, then creates an HTML report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/thedavidweng/skills/roast-my-computer
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 thedavidweng/skills --skill roast-my-computer
Clone the repo
git clone --depth 1 https://github.com/thedavidweng/skills

Made for: Claude Code, Codex.

Or install david-skills, the plugin that ships this one along with the rest of its 22 skills.

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 roast-my-computer

README.md
[![agentmods](https://agentmods.dev/badge/skills/thedavidweng/skills/roast-my-computer/github.svg)](https://agentmods.dev/skills/thedavidweng/skills/roast-my-computer)
Your own site
<a href="https://agentmods.dev/skills/thedavidweng/skills/roast-my-computer"><img src="https://agentmods.dev/badge/skills/thedavidweng/skills/roast-my-computer/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 roast-my-computer

Your own site · 80×15
<a href="https://agentmods.dev/skills/thedavidweng/skills/roast-my-computer"><img src="https://agentmods.dev/badge/skills/thedavidweng/skills/roast-my-computer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,880 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Memory Poisoning · line 93
    Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.
    Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
  • medium Excessive Agency · line 135
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
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 $0.00078 $0.01880
Opus 5 $0.00039 $0.00940
Sonnet 5 $0.00016 $0.00376
Haiku 4.5 $0.00008 $0.00188

Measured 11d ago against content hash eb3df947f46f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

roast-my-computer 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/scan_dev_environment.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

maintenance/roast-my-computer/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Roast My Computer

Generate a local-only developer environment roast. Run the deterministic scanner, then write a branded classic-Macintosh-style HTML report using references/HTML_REPORT_FORMAT.md.

Operating rule

Run locally through the executing agent, using the user's filesystem. Treat memory, paths, dotfiles, source code, credentials, scan JSON, and generated reports as private. Secret values must stay redacted.

Workflow

1. Check for existing reports

Before scanning, check the stable report directory for artifacts from a previous run:

REPORT_DIR="${TMPDIR:-/tmp}/roast-my-computer"
mkdir -p "$REPORT_DIR"
ls -t "$REPORT_DIR"/computer-roast-report-*.html 2>/dev/null | head -1

On Windows use %TEMP%\roast-my-computer instead.

If a previous report exists, ask the user:

  • Open the existing report — open the most recent HTML file with open / xdg-open / start. Skip scanning entirely. Done.
  • Clean up and re-scan — delete all computer-roast-report-*.html and computer-roast-scan.json in the report directory, then continue to step 2.

If no previous report exists, continue directly to step 2.

2. Pick one scope

Offer only these choices unless the user already chose:

  1. Project — scan the current working directory. This fits restricted environments that can only read the active repo/workspace.
  2. Global — let the agent use memory/context to add the user's high-frequency folders, then scan those plus common macOS/Linux/Windows developer locations. Tell the user this is more accurate and may require approving extra filesystem access.

Use Project for "this repo", "current workspace", "safe", or permission-limited runs. Use Global for "my computer", "full roast", "most accurate", or when the user wants the agent to use memory.

3. Build roots

For Project, use the current working directory. Add explicit paths only when the user supplied them.

For Global, first use the agent's memory/context to identify likely user folders: frequent repos, workspaces, design assets, downloads, monorepos, or tool-specific config locations. Then add common defaults from references/DIRECTORY_TARGETS.md.

Read the full file on GitHub · 155 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. 11d ago First seen · 155 lines · 78 tokens per session scan A eb3df947f46f

Subscribe to this mod's changes

roast-my-computer is a skill published in the GitHub repository thedavidweng/skills (11 stars, last pushed 6d ago), licensed MIT. It adds 78 tokens to every session and 1,880 once invoked, about $0.0004 per session on Opus 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 2026-08-30.