Repo of the day: Skill Seekers, from docs, repos and PDFs to Skills for Claude

🔍 What Skill Seekers is

Skill Seekers is a Python command-line tool (with an MCP server too) that converts a knowledge source into a Skill for Claude. The project describes itself as «the data layer for AI systems» — that's homepage language, but the concrete idea underneath is simple: you prep the knowledge once and export it to several targets. The declared supported sources are 18: besides documentation sites, GitHub repos and PDFs, it handles Word, EPUB, Jupyter notebooks, OpenAPI, PowerPoint, Confluence wikis, Notion pages, chat exports and more.

The result isn't a blob of text pasted into a prompt, but a structured Skill: a `SKILL.md` with examples, a `references/` folder with categorized content (API, guides, tutorials) and room for scripts and assets. From there you load it into Claude Code or onto claude.ai. Behind the name are the numbers of a very active project — MIT license, over 14,000 stars, version 3.7.0, more than 700 commits and 3,700+ tests — useful for framing it, but the value for us is what it does, not the badge.

repo       yusufkaraaslan/Skill_Seekers
what       turns documentation/repos/PDFs into Skills for Claude and other LLMs
how        Python CLI `skill-seekers` + MCP server (40 tools)
sources    18 types: docs, GitHub, PDF, Word, EPUB, notebooks, OpenAPI, Notion...
extra      conflict detection: documented API vs real code
license    MIT · Python 3.10+ · v3.7.0, 3,700+ tests

⚙️ Your first Skill in three commands

Installation is that of a Python package from PyPI; you need Python 3.10 or higher and Git. From there the shortest path is three commands: install, create the Skill from a source, package it for the target platform. The source for the `create` command is flexible: a documentation URL, a GitHub `owner/repo`, or the path to a PDF. There's also a wizard, `skill-seekers-setup`, to configure keys and preferences the first time.

Under the hood, `create` runs the pipeline (scraping, categorization, `SKILL.md` generation), while `package` produces the zip ready for the target — for example `--target claude`. Anyone who prefers to work from a config file can copy a preset and edit it: Skill Seekers ships several ready-made ones for well-known libraries.

# 1. Install the package (Python 3.10+, Git)
pip install skill-seekers

# 2. Create the Skill from any source...
skill-seekers create https://docs.react.dev/     # a documentation site
skill-seekers create facebook/react              # a GitHub repository
skill-seekers create manual.pdf                  # a PDF

# 3. Package the Skill for the platform
skill-seekers package output/react --target claude

🧩 How a Skill is born: from scraping to SKILL.md

The pipeline is easy to read. First Skill Seekers checks whether the site exposes an `llms.txt` file (the compact version made for LLMs); then it scrapes the pages, categorizes them by type — API, guides, tutorials — and finally moves to the «enhancement» phase, where a model generates the `SKILL.md` with real examples. The last step packages everything into a zip ready for the platform.

The interesting detail is the enhancement phase: it can run in LOCAL mode, with no API key, using your Claude Code session (Max plan), or via API with a key. In LOCAL, generating the `SKILL.md` takes a few dozen seconds and sends nothing to an external service beyond your already-in-use agent. You can also combine multiple sources into a single Skill, handy when a library's knowledge is scattered across a site, a repo and a reference PDF.

🔎 Conflict detection: when the documentation lies

This is the feature that convinced me most. Skill Seekers doesn't just read the documentation: it compares it with the real code, parsing the AST for Python, JavaScript, TypeScript, Java, C++ and Go. The result is a report flagging four kinds of discrepancy — a function present in the docs but not in the code, one present in the code but undocumented, a signature that doesn't match, a description that doesn't add up. It's exactly the kind of silent drift that sends an agent off track: if the docs say one thing and the code does another, the agent usually picks the wrong one.

Conflict resolution can be rule-based or model-driven, and the value goes beyond the single Skill: it's also a way to surface the gaps in your own documentation before someone else finds them. Moving this check into the knowledge prep — not after it — is the part to imitate.

Conflict report · react/hooks
----------------------------------------
🔴 missing in code    useLegacyEffect()   cited in the docs, absent from the source
🟡 missing in docs    useActionState()    present in the code, undocumented
⚠️ signature mismatch useMemo(fn, deps)   the docs omit the second argument
ℹ️ description drift  useId()             docs text not aligned with behavior

🔌 Bringing the Skill into Claude Code (and the MCP)

Once generated, the Skill has to go where the agent finds it. The `install-agent` command installs it directly: with `--agent claude` it lands in `~/.claude/skills/`, so it's available as a global Claude Code Skill; with `--agent all` it propagates to several agents (Cursor, Copilot, Windsurf and others). Alternatively you upload it to claude.ai: with an `ANTHROPIC_API_KEY` the `--upload` flag does everything, or you go to claude.ai/skills by hand and upload the zip.

Anyone who wants to stay inside the agent's workflow can install the MCP server (`pip install skill-seekers[mcp]` and `./setup_mcp.sh`): from there Claude Code exposes the Skill Seekers tools and you just ask it in natural language to prepare and upload a Skill. It's the same spirit as adding an MCP to the agent: you give it a new capability without leaving the shell.

# Install the Skill into the agent (writes to ~/.claude/skills/)
skill-seekers install-agent output/react/ --agent claude
skill-seekers install-agent output/react/ --agent all

# Or upload the zip to claude.ai (API key required)
export ANTHROPIC_API_KEY=sk-ant-...
skill-seekers package output/react/ --upload

# MCP server: then ask the agent to prepare the Skill
pip install skill-seekers[mcp]
./setup_mcp.sh

🏢 Why it matters, for developers and SMBs

For developers, the win is removing an everyday friction: the Skill the agent uses is current with the real documentation, not with its training. Fewer invented calls, fewer manual fixes, and onboarding onto a new library becomes a command instead of a series of copy-pastes. The knowledge, moreover, is versionable and reusable: you generate it once and share it across the team.

For an SMB the point is different: internal documentation — PDF manuals, Confluence wikis, Notion pages — can become a Skill the agent consults, and conflict detection surfaces where those documents are now stale compared to the software. One governance aspect to understand: the enhancement phase passes the content to a model (your Claude Max session in LOCAL, or an API with a key), so it's worth knowing what you feed it. But it's upstream prep, not a runtime dependency, and that makes it more legible.

⚠️ Limits and caveats before adopting it

Some honesty. The README is openly showcase-toned («99% faster», «the data layer for AI»): these are the project's claims, not independent measurements, and should be read that way. There's also a version drift to watch: the documentation shows commands marked as «not available in v3.7.0», a sign the README runs ahead of the releases — always check with the installed CLI's help. The enhancement phase, mandatory in the `install` command, requires the Claude Max plan or an API key, and the quality of the `SKILL.md` depends on the model and the source.

On the practical side, scraping large sites takes time (15 to 45 minutes in the project's estimate); very dynamic sites may need the browser extra, and scanned PDFs must be passed with OCR. Mind GitHub's rate limits: anonymous is 60 requests per hour, which become 5,000 by setting a `GITHUB_TOKEN`. Finally it's a young, fast-moving project: powerful, but with an API and commands that can change between versions.

• Marketing-toned README: the figures («99% faster») are the project's, not third-party measurements.
• Version drift: some documented commands don't exist in v3.7.0 — check the CLI help.
• Enhancement isn't free: it needs the Claude Max plan or an API key; quality depends on model and source.
• Scraping is slow, with extras: large sites 15-45 min, SPAs need the browser, scanned PDFs need OCR.
• GitHub rate limit: 60 requests/hour anonymous, 5,000 with a GITHUB_TOKEN.

✅ Where to start

The low-risk path is this: install the package and generate a Skill from a small documentation set you know well, so you can judge the quality of the `SKILL.md` and the references. Then install it with `install-agent --agent claude` and ask Claude something only that documentation can answer: you'll immediately see whether the Skill works. As a third step, try conflict detection on a repo where you know docs and code diverge, and read the report.

Zooming out, the signal isn't the single tool but the habit it proposes: treating documentation as a first-class input for agents, to prepare once and reuse. For an SMB putting AI into real processes, the concrete lesson is exactly this — your manuals and wikis can become a Skill, and it's worth knowing when those documents have stopped telling the truth about the software.

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