agentmods.dev

acquire-codebase-knowledge

A process for learning and documenting an existing codebase—the files and systems that make up a software project. It creates documents about the project's tools, structure, design, conventions, connections to other systems, testing, and concerns.

In plain words
What is it for?
Use it when onboarding to a repository, mapping its architecture, or creating evidence-based documentation in docs/codebase/.
Why use it?
It gives developers a traceable overview of an unfamiliar project while marking unknown information instead of guessing.

Skill for Claude CodeCodex

259 3mo ago A scan 82 tokens original MIT
View the source file boshi-xixixi/TraeSkill
Install

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.

agentmods
npx agentmods add skills/boshi-xixixi/traeskill/acquire-codebase-knowledge
Any agent
npx skills add boshi-xixixi/TraeSkill --skill acquire-codebase-knowledge
Clone the repo
git clone --depth 1 https://github.com/boshi-xixixi/TraeSkill

Made for: Claude Code, Codex.

Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,156 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00082 $0.02156
Opus 5 $0.00041 $0.01078
Sonnet 5 $0.00016 $0.00431
Haiku 4.5 $0.00008 $0.00216

Measured 2d ago against content hash 7ca01711e161, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/scan.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.trae/Skills/.agents/skills/acquire-codebase-knowledge/SKILL.md · 175 lines

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:

  1. Exactly these files exist in docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, CONCERNS.md.
  2. Every claim is traceable to source files, config, or terminal output.
  3. Unknowns are marked as [TODO]; intent-dependent decisions are marked [ASK USER].
  4. Every document includes a short "evidence" list with concrete file paths.
  5. 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"):

  1. Always run Phase 1 in full.
  2. Fully complete focus-area documents first.
  3. For non-focus documents not yet analyzed, keep required sections present and mark unknowns as [TODO].
  4. Still run the Phase 4 validation loop on all seven documents before final output.

Phase 1: Scan and Read Intent

  1. Run the scan script from the target project root:

    python3 "$SKILL_ROOT/scripts/scan.py" --output docs/codebase/.codebase-scan.txt

    Where $SKILL_ROOT is 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

  2. Search for PRD, TRD, README, ROADMAP, SPEC, DESIGN files and read them.

  3. Summarise the stated project intent before reading any source code.

Read the full file on GitHub · 175 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 175 lines · 82 tokens per session scan A 7ca01711e161

Subscribe to this mod's changes

acquire-codebase-knowledge is a skill published in the GitHub repository boshi-xixixi/TraeSkill (259 stars, last pushed 3mo ago), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

PlanningWriting and docs
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