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 agentmods add commands/acaprino/daodan/analyzegit clone --depth 1 https://github.com/acaprino/daodanWrote 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/commands/acaprino/daodan/analyze)<a href="https://agentmods.dev/commands/acaprino/daodan/analyze"><img src="https://agentmods.dev/badge/commands/acaprino/daodan/analyze.svg" alt="Measured on agentmods" 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 | $0.00084 | $0.06809 |
| Opus 5 | $0.00042 | $0.03404 |
| Sonnet 5 | $0.00017 | $0.01362 |
| Haiku 4.5 | $0.00008 | $0.00681 |
Grade C, and why
analyze scanned grade C with 1 finding 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 4d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
- `.npmrc`, `.pypirc`, `.netrc` - auth tokens How it starts
The opening of the file, as written. The whole thing — 648 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase X-Ray Analysis
CRITICAL RULES
- Execute phases in order. Unless
--phase Nskips to a specific phase. - Write output files. Each phase produces a file in the run directory for context passing.
- Run isolation. All writes go to
$RUN_DIRuntil the publish step. Never write phase files to the.deep-dive/root during analysis: concurrent runs share that root. - Stop at checkpoints. Confirm scope before starting analysis and before applying changes.
- Never enter plan mode. Execute immediately.
- Code is ground truth. Document what the code actually does, not what you think it should do.
Tool Integration
The scripts in ${CLAUDE_PLUGIN_ROOT}/skills/analyze/scripts/ are language-aware and support: Python, Java, JavaScript, TypeScript (incl. TSX/JSX), SQL, PL/SQL, Rust. You MUST use them instead of manual file reading whenever the target file matches one of those languages.
- Phase 1-2 (Structure): Use
ast_parser.pyfor class/function/import extraction andclassifier.pyfor file classification. Do NOT attempt to parse AST manually or count imports with grep. - Phase 5 (Risks): Use
usage_finder.pyto trace symbol usages across the codebase. Multi-language: matches Pythonfrom/import, Javaimport, JS/TSimport/require, Rustuse, etc. - Phase 6 (Docs): Use
doc_review.pyfor link validation and marker checks, andrewrite_comments.pyfor multi-language comment quality analysis (Python#/docstrings, Java/JS/TS////* *// Javadoc / JSDoc, SQL/PL-SQL--//* */, Rust/// rustdoc).
For unsupported languages, use the Read tool and Grep tool directly. Tree-sitter is optional (see Prerequisites in the codebase-xray:analyze skill): when tree-sitter-language-pack is installed, Java/JS/TS/Rust use the tree-sitter parsers for higher fidelity; otherwise a regex fallback is used. Python always uses the stdlib ast module. SQL/PL-SQL use a regex-based DDL extractor.
Do NOT use raw bash commands (cat, grep, find) to extract structure when a dedicated script exists. The scripts use real parsers, which are faster, more accurate, and consume fewer tokens than reading files line by line.
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.
- 4d ago First seen · 648 lines · 84 tokens per session scan C b3810e4e0a4c
analyze is a command published in the GitHub repository acaprino/daodan (8 stars, last pushed 9d ago), licensed MIT. It adds 84 tokens to every session and 6,809 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.