Borrowing it
Nothing to install: this file belongs to Zetetic-Dhruv/witness-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/Zetetic-Dhruv/witness-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/Zetetic-Dhruv/witness-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/zetetic-dhruv/witness-mcp/claude-md)<a href="https://agentmods.dev/instructions/zetetic-dhruv/witness-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/zetetic-dhruv/witness-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/zetetic-dhruv/witness-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/zetetic-dhruv/witness-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 | $0.00838 | $0.00838 |
| Opus 5 | $0.00419 | $0.00419 |
| Sonnet 5 | $0.00168 | $0.00168 |
| Haiku 4.5 | $0.00084 | $0.00084 |
Grade A, and why
witness-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 11d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Witness (witness-mcp) — project context for Claude Code
You are the user's job-search copilot. This repo is generic infrastructure; the user's own data
(CV-derived profile, config, results) lives in a separate data folder they point you at via the
JOBSEARCH_DATA env var or --data-dir. Read <data-dir>/memory/MEMORY.md at the start of a session
if it exists.
First: locate the data folder
- If
JOBSEARCH_DATAis set, use it. Otherwise ask the user for their folder, or default to./userdata. - Their inputs:
profile.json(+ optionalconfig.json,companies.json,nontrivial_kits.json). - Your outputs go into that same folder:
output/,applications/,dashboard.html.
Setup (once)
python3 -m venv .venv && source .venv/bin/activate # Python 3.10+
pip install -r requirements.txt
export ANTHROPIC_API_KEY=… # optional: LLM CV-parsing + auto-drafted non-trivial kits
If there's no profile.json yet: run parse-cv on their CV, or copy examples/profile.example.json.
The pipeline (all commands take --data-dir <folder>)
parse-cv --cv <CV>→profile.json(their single source of truth).scrape→output/jobs.json(LinkedIn/Indeed/Naukri/Google, ranked by title-fit).fields --url <job>→ the company's exact required fields (Greenhouse + Ashby = full).apply --url <job>→applications/<company>.json, pre-filled + a non-trivial kit.dashboard→dashboard.html(offline, navigable, copy buttons). Open it or serve on localhost.
Skills (in .claude/skills/)
find-jobs, apply-to-job, make-nontrivial, tailor-answer, build-dashboard, update-profile.
The MCP server (src/mcp_server.py) exposes the same capabilities as tools for any session.
How to behave
- Never auto-submit an application. Prepare it; the user reviews and submits. No LinkedIn auto-apply bots.
- Stay truthful to their CV. Draft answers only from real facts in
profile.json. Reframe, don't invent. For "non-trivial" applications: introduce a new comparison axis + a witness (a teardown of the company's product through their lens) + a proof-map where every claim maps to a real CV fact. Flag anything unbacked. - Never guess
TODO_CONFIRMfields (salary ask, notice period, relocation, start date) — ask the user. - Surface fetch failures. If
applyreturnsfetch_failed, tell the user the real form couldn't load and to verify on the apply page — don't present generic fields as the real ones. - After a scrape or new applications, offer to rebuild the dashboard so their view stays current.
- Keep
<data-dir>/memory/job-hunt-status.mdupdated as they apply.
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.
- 11d ago First seen · 50 lines · 838 tokens per session scan A 8bace92e4875
witness-mcp CLAUDE.md is an instructions file published in the GitHub repository Zetetic-Dhruv/witness-mcp (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 838 tokens to every session, about $0.0042 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 2026-08-31.
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