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.
npx skills add TonyCasey/lisa --skill reviewgit clone --depth 1 https://github.com/TonyCasey/lisaWrote 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.
[](https://agentmods.dev/skills/tonycasey/lisa/review)<a href="https://agentmods.dev/skills/tonycasey/lisa/review"><img src="https://agentmods.dev/badge/skills/tonycasey/lisa/review/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.
<a href="https://agentmods.dev/skills/tonycasey/lisa/review"><img src="https://agentmods.dev/badge/skills/tonycasey/lisa/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00802 |
| Opus 5 | $0.00000 | $0.00401 |
| Sonnet 5 | $0.00000 | $0.00160 |
| Haiku 4.5 | $0.00000 | $0.00080 |
Grade A, and why
review 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 8d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Skill
Purpose
Automatically analyzes a codebase when Lisa is installed, creating a foundational memory of the project structure, technologies, and patterns. This review serves as context for all future Claude sessions.
Triggers
Use when the user says things like:
- "run review"
- "analyze this codebase"
- "scan the project"
- "what is this project about"
- "refresh codebase summary"
How to use
Automatic (during npm install)
The review runs automatically when Lisa is installed via npm install @tonycasey/lisa. It:
- Detects if the folder is a codebase
- Runs static analysis (language, framework, structure)
- Stores result as first memory
- Queues background AI enrichment
Manual commands
# Run review (or re-run with --force)
lisa review run [--force]
# Show current review
lisa review show
# Check status (done, enriched, etc.)
lisa review status
I/O contract
Static analysis output
{
"status": "ok",
"action": "run",
"result": {
"version": "1.0",
"project": { "name": "lisa", "path": "/dev/lisa", "groupId": "dev-lisa" },
"codebase": {
"language": "TypeScript",
"languages": ["TypeScript", "JavaScript"],
"framework": null,
"buildTools": ["npm", "tsc"]
},
"structure": {
"entryPoints": ["src/cli.ts", "src/index.ts"],
"mainModules": ["src/domain/", "src/infrastructure/"],
"testDirs": ["tests/"]
},
"dependencies": {
"count": 12,
"noteworthy": ["commander", "fs-extra", "@anthropic-ai/claude-code"]
},
"patterns": {
"architecture": "clean-architecture",
"testing": "node-test"
},
"metrics": {
"fileCount": 45,
"hasTests": true,
"hasDocumentation": true
}
},
"summary": "TypeScript CLI project with clean-architecture pattern..."
}
Show output
{
"status": "ok",
"action": "show",
"review": "TypeScript CLI project with clean-architecture pattern...",
"enriched": true,
"timestamp": "2026-01-11T15:30:00Z"
}
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.
- 8d ago First seen · 120 lines · 0 tokens per session scan A 794d91a326a5
review is a skill published in the GitHub repository TonyCasey/lisa (9 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 802 tokens. 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.
Other skills, from other repositories
user-profile-keeper
Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration…
install-memu
Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.
log-session
Append a structured entry to the project's session log (docs/LOGS.md): what was done this session, files touched, decisions taken, and the next step — so the next session (or another person) can pick up the thread without re-reading git history. Run it before /clear, before closing Claude Code, or at any natural…
lore
Long-term Markdown project memory for AI coding agents. Use when the user wants to record, recall, audit, sync, or compress project decisions, architecture, conventions, monorepo scopes, or .lore/ entries, including natural-language requests like "remember this decision" or explicit lore…
memory
Use Infinity Context MCP to search, propose, and manage durable project memory.
end
Close a work session — log what happened, update state and the decision log, propose durable memory updates, and check for uncommitted or unpushed work.