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 OKHP3/skillz --skill acquire-codebase-knowledgegit clone --depth 1 https://github.com/OKHP3/skillzWrote 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/okhp3/skillz/acquire-codebase-knowledge)<a href="https://agentmods.dev/skills/okhp3/skillz/acquire-codebase-knowledge"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/acquire-codebase-knowledge/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/okhp3/skillz/acquire-codebase-knowledge"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/acquire-codebase-knowledge.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 | $0.00082 | $0.02156 |
| Opus 5 | $0.00041 | $0.01078 |
| Sonnet 5 | $0.00016 | $0.00431 |
| Haiku 4.5 | $0.00008 | $0.00216 |
Grade A, and why
acquire-codebase-knowledge 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 6d 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.
This is a copy
100% identical to acquire-codebase-knowledge — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Acquire Codebase Knowledge
Produces seven populated documents in docs/codebase/ covering everything needed to work effectively on the project. Only document what is verifiable from files or terminal output — never infer or assume.
Output Contract (Required)
Before finishing, all of the following must be true:
- Exactly these files exist in
docs/codebase/:STACK.md,STRUCTURE.md,ARCHITECTURE.md,CONVENTIONS.md,INTEGRATIONS.md,TESTING.md,CONCERNS.md. - Every claim is traceable to source files, config, or terminal output.
- Unknowns are marked as
[TODO]; intent-dependent decisions are marked[ASK USER]. - Every document includes a short "evidence" list with concrete file paths.
- Final response includes numbered
[ASK USER]questions and intent-vs-reality divergences.
Workflow
Copy and track this checklist:
- [ ] Phase 1: Run scan, read intent documents
- [ ] Phase 2: Investigate each documentation area
- [ ] Phase 3: Populate all seven docs in docs/codebase/
- [ ] Phase 4: Validate docs, present findings, resolve all [ASK USER] items
Focus Area Mode
If the user supplies a focus area (for example: "architecture only" or "testing and concerns"):
- Always run Phase 1 in full.
- Fully complete focus-area documents first.
- For non-focus documents not yet analyzed, keep required sections present and mark unknowns as
[TODO]. - Still run the Phase 4 validation loop on all seven documents before final output.
Phase 1: Scan and Read Intent
-
Run the scan script from the target project root:
python3 "$SKILL_ROOT/scripts/scan.py" --output docs/codebase/.codebase-scan.txtWhere
$SKILL_ROOTis the absolute path to the skill folder. Works on Windows, macOS, and Linux.Quick start: If you have the path inline:
python3 /absolute/path/to/skills/acquire-codebase-knowledge/scripts/scan.py --output docs/codebase/.codebase-scan.txt -
Search for
PRD,TRD,README,ROADMAP,SPEC,DESIGNfiles and read them. -
Summarise the stated project intent before reading any source code.
What ships with it
10 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.
- assets/templates/ARCHITECTURE.md 1.2 KB
- assets/templates/CONCERNS.md 1.7 KB
- assets/templates/CONVENTIONS.md 1.3 KB
- assets/templates/INTEGRATIONS.md 1.3 KB
- assets/templates/STACK.md 1.3 KB
- assets/templates/STRUCTURE.md 1.1 KB
- assets/templates/TESTING.md 1.3 KB
- references/inquiry-checkpoints.md 4.1 KB
- references/stack-detection.md 5.5 KB
- scripts/scan.py 23 KB runs code
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.
- 6d ago First seen · 175 lines · 82 tokens per session scan A 7ca01711e161
acquire-codebase-knowledge is a skill published in the GitHub repository OKHP3/skillz (3 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 2,156 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to acquire-codebase-knowledge, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
vastai-sdk
Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.
pm-aarrr
A post-launch product-growth workflow based on AARRR: acquiring users, activating them, retaining them, earning revenue, and gaining referrals.
pm-docs
A workflow for producing product documents such as PRDs, BRDs, and MRDs. These are structured documents describing what a product needs, its business case, and its market.
pm-position
A guided process for defining a product’s market position, value, audience, competitive difference, business model, pricing, and revenue plan. The instructions are written mainly in Chinese.
pm-retro
A retrospective workflow for reviewing a finished agile iteration, recording what happened, and choosing improvements for the next cycle.
product-marketing-copywriter
A marketing-copy tool that analyzes audience problems and product benefits, then creates promotional headlines and body text. Marketing copy is writing intended to explain and promote a product.