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 neuromechanist/research-skills --skill codebase-onboardinggit clone --depth 1 https://github.com/neuromechanist/research-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/neuromechanist/research-skills/codebase-onboarding)<a href="https://agentmods.dev/skills/neuromechanist/research-skills/codebase-onboarding"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/codebase-onboarding/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/neuromechanist/research-skills/codebase-onboarding"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/codebase-onboarding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00094 | $0.01454 |
| Opus 5 | $0.00047 | $0.00727 |
| Sonnet 5 | $0.00019 | $0.00291 |
| Haiku 4.5 | $0.00009 | $0.00145 |
Grade A, and why
codebase-onboarding 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 10d 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.
Codebase and Field Onboarding
Build a verified map of an unfamiliar codebase, project, or research area before planning or editing anything. The output is a reconnaissance report that separates verified facts from assumptions, ending in a one-paragraph diagnosis.
The bootstrap sequence (run in this order)
- Date and inventory. Run
date. List the tree (source dirs, configs, docs). Note the largest files with line counts; they are usually where the complexity lives. - Intent docs before code. Read
AGENTS.md/CLAUDE.md,README,.context/(plan.md, research.md, decisions/), design docs. These say what the project is SUPPOSED to be. - Code next. Read entry points, the main loop or route table, and the one or two central types. Do not read everything; read what the intent docs point at.
- History.
git log --oneline -20, open issues and PRs (gh issue list,gh pr list). What was recently worked on, what is planned, what is stuck. - What is ACTUALLY running. Processes, containers (
docker ps -a), data directories on disk, deployed endpoints, cron entries. This step distinguishes "designed" from "operated": if the design exists in code and issues but there is no runtime data on disk, the true next step is "turn it on and calibrate", not "design more". - Verify the specific claim. Whatever question triggered this onboarding ("does compaction work?", "is the migration done?"): grep the source and check live state for it directly. Absence of a script is not absence of work; completed one-off scripts get deleted.
- Probe third-party surfaces. Before planning against any SDK or API, run one cheap introspection command against the INSTALLED version (import it, list the dataclass fields, hit the version endpoint). Treat your trained-in knowledge of library internals as a hypothesis.
- Diagnose in one paragraph. State plainly what exists, what runs, what is broken or missing, and what that implies, before proposing any action.
What ships with it
1 file 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.
- 10d ago First seen · 120 lines · 94 tokens per session scan A 2c7fd3d8f09c
codebase-onboarding is a skill published in the GitHub repository neuromechanist/research-skills (45 stars, last pushed 7d ago), licensed BSD-3-Clause. It adds 94 tokens to every session and 1,454 once invoked, about $0.0005 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.
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