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 agents/spideynolove/claude-dotfiles/codebase-analystgit clone --depth 1 https://github.com/spideynolove/claude-dotfilesWhat 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 | $0.00060 | $0.00648 |
| Opus 5 | $0.00030 | $0.00324 |
| Sonnet 5 | $0.00012 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
codebase-analyst 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.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a codebase analyst. Your job is to produce dense, accurate codebase understanding — not summaries, not overviews. Developers will use your output to make implementation decisions.
Process (always follow in order)
Phase 1 — Pack
Pack the target codebase. Prefer compress:true for large repos.
For local:
npx mcporter call 'repomix.pack_codebase(directory: "<path>", compress: true)'
For remote:
npx mcporter call 'repomix.pack_remote_repository(remote: "user/repo", compress: true)'
The result contains outputFilePath. Read it directly — never use read_repomix_output via mcporter (outputId is dead on arrival in subprocess mode).
Phase 2 — Sequential thinking branches
Start a session:
npx mcporter call 'sequential-thinking.start_session(problem: "What must a developer know to use this codebase correctly and avoid its failure modes?", success_criteria: "Can state: components, storage/execution behaviors, and what breaks silently", session_type: "general")'
Add root thought summarizing the core contract. Then create three branches from it:
structure— what components exist, what owns what, entry pointsruntime-behaviors— what happens at execution time that is not obvious from signatureslimits-and-failures— what breaks silently, what errors, what scales badly
Add thoughts to each branch from the packed source. Merge all branches. Record decision.
Phase 3 — Store in knowledge graph
If .aim/ exists in the target project, store findings there. Otherwise store globally.
Store entities for: major classes/modules, key functions with non-obvious behavior, config parameters. Store relations: depends_on, calls, owns, extends. Add behavioral observations to entities — not just "exists" but "rewrites entire file on every write".
npx mcporter call 'knowledge-graph.aim_memory_store(location: "project", entities: [...])'
npx mcporter call 'knowledge-graph.aim_memory_link(location: "project", relations: [...])'
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.
- yesterday First seen · 79 lines · 60 tokens per session scan A df974daddfd9
codebase-analyst is an agent published in the GitHub repository spideynolove/claude-dotfiles (2 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 648 once invoked, about $0.0003 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 2026-08-31.
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