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 skills/sjarmak/coding-agent-workflows/codebase-onboardingnpx skills add sjarmak/coding-agent-workflows --skill codebase-onboardinggit clone --depth 1 https://github.com/sjarmak/coding-agent-workflowsWhat 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 | $0.00053 | $0.01964 |
| Opus 5 | $0.00026 | $0.00982 |
| Sonnet 5 | $0.00011 | $0.00393 |
| Haiku 4.5 | $0.00005 | $0.00196 |
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 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.
This is a copy
97% identical to ecc-codebase-onboarding — 52 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Onboarding
Systematically analyze an unfamiliar codebase and produce a structured onboarding guide. Designed for developers joining a new project or setting up Claude Code in an existing repo for the first time.
When to Use
- First time opening a project with Claude Code
- Joining a new team or repository
- User asks "help me understand this codebase"
- User asks to generate a CLAUDE.md for a project
- User says "onboard me" or "walk me through this repo"
How It Works
Phase 1: Reconnaissance
Gather raw signals about the project without reading every file. Run these checks in parallel:
1. Package manifest detection
→ package.json, go.mod, Cargo.toml, pyproject.toml, pom.xml, build.gradle,
Gemfile, composer.json, mix.exs, pubspec.yaml
2. Framework fingerprinting
→ next.config.*, nuxt.config.*, angular.json, vite.config.*,
django settings, flask app factory, fastapi main, rails config
3. Entry point identification
→ main.*, index.*, app.*, server.*, cmd/, src/main/
4. Directory structure snapshot
→ Top 2 levels of the directory tree, ignoring node_modules, vendor,
.git, dist, build, __pycache__, .next
5. Config and tooling detection
→ .eslintrc*, .prettierrc*, tsconfig.json, Makefile, Dockerfile,
docker-compose*, .github/workflows/, .env.example, CI configs
6. Test structure detection
→ tests/, test/, __tests__/, *_test.go, *.spec.ts, *.test.js,
pytest.ini, jest.config.*, vitest.config.*
Phase 2: Architecture Mapping
From the reconnaissance data, identify:
Tech Stack
- Language(s) and version constraints
- Framework(s) and major libraries
- Database(s) and ORMs
- Build tools and bundlers
- CI/CD platform
Architecture Pattern
- Monolith, monorepo, microservices, or serverless
- Frontend/backend split or full-stack
- API style: REST, GraphQL, gRPC, tRPC
Key Directories Map the top-level directories to their purpose:
src/components/ → React UI components
src/api/ → API route handlers
src/lib/ → Shared utilities
src/db/ → Database models and migrations
tests/ → Test suites
scripts/ → Build and deployment scripts
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 · 234 lines · 53 tokens per session scan A 0c6c03ee8df6
codebase-onboarding is a skill published in the GitHub repository sjarmak/coding-agent-workflows (2 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 1,964 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to ecc-codebase-onboarding, differing in 52 lines, and is treated as a copy.
Other skills, from other repositories
browser-trace
Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page…
planning-with-files
Manus-style persistent file-based planning for AI coding agents: keeps taskplan.md, findings.md, and progress.md on disk so work survives context loss and /clear. Use when asked to plan out, break down, or organize a multi-step project, research task, or any work requiring 5+ tool calls. Supports automatic session…
ai-elements
Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
exa-search
Use Exa MCP for current web, code/docs, company, people, and page-fetch research. Prefer current hosted tool schemas and note deprecated tools.
ontoly-software-graph
Use Ontoly's deterministic Software Graph and MCP capabilities for repository architecture, request tracing, dependency analysis, configuration lookup, and impact analysis before falling back to source search.
product-decision-agent
中文产品决策 Agent。用于中国大陆互联网产品、运营、增长、商业化、数据、项目推进和组织协作场景：产品规划、需求分析、PRD、需求优先级、排期、版本规划、Roadmap、MVP、灰度、上线、迭代、增长停滞、拉新、投放、渠道、裂变、CAC、LTV、ROI、留存、转化、DAU/MAU、GMV、漏斗、社区运营、内容供给、创作者、用户运营、活动运营、私域、会员、定价、指标异常、数据口径、埋点、A/B…