Borrowing it
Nothing to install: this file belongs to negrueu/linkedin-company-admin-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/negrueu/linkedin-company-admin-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/negrueu/linkedin-company-admin-mcpWrote 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/instructions/negrueu/linkedin-company-admin-mcp/claude-md)<a href="https://agentmods.dev/instructions/negrueu/linkedin-company-admin-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/negrueu/linkedin-company-admin-mcp/claude-md/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/instructions/negrueu/linkedin-company-admin-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/negrueu/linkedin-company-admin-mcp/claude-md.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 | $0.00499 | $0.00499 |
| Opus 5.5 | $0.00200 | $0.00200 |
| Sonnet 5.5 | $0.00100 | $0.00100 |
| Haiku 4.5 | $0.00050 | $0.00050 |
Grade A, and why
linkedin-company-admin-mcp CLAUDE.md 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 2mo 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guidance for AI agents (Claude, etc.)
Project purpose
MCP server exposing ~24 tools for LinkedIn Company Page administration, plus a few Personal -> Company bridge tools for employee advocacy workflows.
Complementary to stickerdaniel/linkedin-mcp-server (which covers personal LinkedIn). We intentionally do NOT re-implement feed reading, personal messaging, personal search, or connection management.
Core principles
- Browser-first write path via Patchright (headless Chromium, stealth). No Voyager internal API, no Community Management API (yet).
- Zero credentials stored. Login is always interactive (
--loginopens visible Chromium). Session lives in a persistent profile dir chmod'd to0o700on Unix. - Selectors must be aria-label / role / innerText based. NEVER obfuscated CSS classes like
_8898b74d__foo. When LinkedIn ships a new UI, the only file that changes isselectors/__init__.py. - No god-files. Max ~300 lines per file. If it gets bigger, split by responsibility.
- Tests matter. Unit tests for selectors + URN parsing + config. Integration tests use captured HTML fixtures (deterministic). E2E is manual-only (marker
@slow). - Rate limiting is applied, not just imported. Every write tool is wrapped in
@rate_limited(...). - Systematic debugging before fixing. Never "try strategy N" without root cause analysis.
File layout
Flat layout (no src/). Package is linkedin_company_admin_mcp/ at the repo root. Tool groups live in tools/*.py, each exposing a register_X_tools(mcp: FastMCP) factory called from server.py::create_mcp_server().
Commit rules
- Commit messages in English (only).
- Prefer small, focused commits with imperative subject ("add company_read_page tool").
- Any bug fix commit must include a
Root cause:line explaining what was actually wrong.
Running locally
uv sync
uv run pytest tests/unit # fast
uv run ruff check .
uv run ruff format --check .
uv run mypy linkedin_company_admin_mcp
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.
- 2mo ago First seen · 40 lines · 499 tokens per session scan A 0ecac43ead33
linkedin-company-admin-mcp CLAUDE.md is an instructions file published in the GitHub repository negrueu/linkedin-company-admin-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 499 tokens to every session, about $0.0020 per session on Opus 5.5. 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 2026-08-31.
linkedin-company-admin-mcp CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| linkedin-company-admin-mcp CLAUDE.mdnegrueu/linkedin-company-admin-mcp | Instructions file | 0 | 6mo ago | MIT | A | 499 | Claude Code |
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