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 The-AI-Directory-Company/agents-and-skills --skill codebase-explorationgit clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote 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/the-ai-directory-company/agents-and-skills/codebase-exploration)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/codebase-exploration"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/codebase-exploration/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/the-ai-directory-company/agents-and-skills/codebase-exploration"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/codebase-exploration.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 | $0.00037 | $0.01377 |
| Opus 5 | $0.00018 | $0.00688 |
| Sonnet 5 | $0.00007 | $0.00275 |
| Haiku 4.5 | $0.00004 | $0.00138 |
Grade A, and why
codebase-exploration 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Exploration
Before you start
Gather the following from the user. If anything is missing, ask before proceeding:
- What is the repository? — URL or local path to the codebase
- What is the goal? — Bug fix, feature addition, general understanding, onboarding, or audit
- What do you already know? — Language, framework, or prior context (even partial)
- What is the scope? — Entire repo, a specific subsystem, or a single feature flow
- What is the time budget? — Quick orientation (30 min) or deep mapping (hours)
Exploration procedure
1. Read the Project Manifest
Start with the files that declare what the project is and how it runs:
README.md,CONTRIBUTING.md,CLAUDE.md— stated architecture, setup, conventionspackage.json,Cargo.toml,pyproject.toml,go.mod— language, dependencies, scriptsDockerfile,docker-compose.yml,.env.example— runtime environment and services- CI config (
.github/workflows/,.gitlab-ci.yml) — build steps reveal the dependency graph
Record: language, framework, build tool, test runner, deployment target.
2. Map the Directory Structure
Run a shallow tree (depth 2-3) and classify each top-level directory:
- Entry points:
src/index.*,app/,cmd/,main.* - Configuration: config files, env schemas, feature flags
- Domain logic: models, services, use-cases, controllers
- Data access: repositories, queries, migrations, ORM schemas
- API surface: routes, handlers, resolvers, RPC definitions
- Shared utilities: libs, helpers, utils, common
- Tests: test directories, fixture files, factories
Sketch a layer diagram: entry point -> routing -> handlers -> domain -> data access -> external services.
3. Trace the Primary Data Flow
Pick the most important user action (e.g., "user signs up", "order is placed") and trace it end-to-end:
- Find the route or entry point that handles it
- Follow the handler into service/domain logic
- Identify every database query, API call, or side effect
- Note the response path back to the caller
- Record each file touched and its role in the flow
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 130 lines · 37 tokens per session scan A 8c29551b0d2a
codebase-exploration is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 1,377 once invoked, about $0.0002 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 2026-09-03.
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