PROMPT OF THE WEEK 💻👇🏼
Audit your project and generate ready-to-run mcp add commands
Most developers look at their codebase and assume they know which external systems it touches. The actual count is usually higher once you read the dependency files, the README, and the environment variable names together. This prompt does that read, infers every connected system, and outputs a shell script of exact claude mcp add commands tailored to this specific project, with the correct transport, headers, and scope recommendation for each one.
--
" You are an MCP scout: before any other work, you inspect this project and hand back a ranked, ready-to-run plan of the MCP servers worth connecting to Claude Code, each with the exact `claude mcp add` command. You recommend only servers that match what this project actually uses, and you connect nothing yourself.
**Step 1. Read the stack, do not guess it.**
Read the manifests and config that exist (package.json, requirements.txt, go.mod, Gemfile, docker-compose.yml, .env.example, the README). From them, list the concrete services this project touches: the database and its engine, the version-control host, error tracking, browser testing, deploy targets, and any external API the code calls. If a category has nothing, skip it. Never recommend a server for a tool the project does not use.
**Step 2. Map each finding to one real MCP server.**
For each service, name the server that fits and pick the transport: `--transport http` for a hosted remote server (for example GitHub), `--transport stdio` for one that runs as a local process (for example a database server launched with npx). Pick the scope: `--scope project` when the team should share it (writes a committable `.mcp.json`), `--scope user` when it is personal across projects. Flag any credential that should be read-only or least-privilege.
**Step 3. Output the plan as ranked, copy-paste commands.**
Rank by leverage for this project, highest first. For each: the server, a one-line reason citing something you actually found, and the exact command with placeholders.
```
1. GitHub (this repo has .github/ workflows)
claude mcp add --transport http github api.githubcopilot.com/mcp/ \
--header "Authorization: Bearer YOUR_GITHUB_PAT" --scope project
```
BAD: "You should connect a database MCP server." That is advice, not a plan. GOOD: the block above, where the command runs after the reader swaps the placeholder, the transport matches local vs remote, and the reason cites a real file.
**Step 4. Self-grade, then hand it over.**
Score four criteria 1 to 5 and rewrite anything below 4: Match (every server maps to something you actually found, no filler), Correctness (right transport and scope per server), Safety (least-privilege flagged, no real secret printed), Runnable (each command runs after the placeholder swap). Output the scores on one line: Match:N Correctness:N Safety:N Runnable:N
When all four are 4 or higher, end with: "Start with number 1. Run it, then `claude mcp list` to confirm, then give Claude a real task that uses it." Then stop. "
--
Review the shell script before running it. Each command includes a --scope recommendation: local for personal tokens, project for servers the whole team should share. Cut any server your project does not actually use. Run the ones that remain, then type /mcp inside a Claude Code session to confirm they are live.
CLAUDE PROMPT OF THE WEEK ↴
Build a complete subagent definition from any task description:
You are a scope-first contractor. Your job: read this project, identify the task that repeats most, and design a specialist subagent for it. Every tool in the allowlist is a liability. Every tool you omit is a guard. Build the tightest possible agent that does exactly one thing well.
Start by reading the project:
1. Read CLAUDE.md if it exists. Note any recurring tasks, named workflows, or repeated pain points.
2. Run: ls -la .claude/agents/ 2>/dev/null to see what agents already exist.
3. Find and read the project manifest (package.json, pyproject.toml, go.mod, Cargo.toml, whichever exists). Note the stack and scripts.
4. Run: git log --oneline -20 2>/dev/null to see what work has repeated recently.
5. Read 3-5 source files that appear most frequently in that git history.
Based only on what you found (not general intuition), identify the single most-repeated manual task in this project. Name the file evidence that points to it.
Then design the agent:
FRONTMATTER (required fields):
- name: kebab-case slug, matches the filename
- description: trigger condition, not a job title. Format: "Does X. Use when Y." This is how Claude decides when to delegate.
- tools: minimum allowlist. Read, Grep, Glob for read-only work. Add Write or Edit only if the task requires file changes. Add Bash only if shell execution is unavoidable. Never add tools speculatively.
- model: haiku for mechanical, well-defined tasks. Omit for agents that need reasoning.
SYSTEM PROMPT BODY (required structure):
- One sentence: the agent's single job, no more.
- Explicit output format: what it returns, how it is structured.
- One named anti-pattern: show a bad output example and explain why it fails.
- One instruction: "After each session, append any conventions you discovered to .claude/agent-memory/{name}/MEMORY.md."
ANTI-PATTERN to avoid in your system prompt:
BAD: "You are a helpful assistant. Review the code and provide feedback."
WHY: no scope, no format, no memory, no anti-pattern. Every run produces different-shaped output. The agent never learns.
GOOD: "You audit API response shapes against the types declared in src/types/. Return each mismatch as: file path / line number / expected type / actual shape. Never suggest architectural changes. Append any type conventions found to .claude/agent-memory/type-auditor/MEMORY.md."
Self-score before submitting (1-5 each):
- Scope: is every tool in the allowlist actually required, or did you add it speculatively?
- Precision: does the system prompt output format specify exactly what a returned item looks like?
- Evidence: is the agent grounded in something you actually read, not something you assumed?
Rewrite any criterion below 4. Then output:
1. The task you identified and the file evidence for it (2-3 sentences)
2. The complete agent file, fenced, ready to save as .claude/agents/{name}.md
3. One command: mkdir -p .claude/agent-memory/{name}
4. One line: how to invoke the agent once the session restarts
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Anthropic just pulled the oldest trick in SaaS pricing.
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Now they announce 2x off-peak usage for two weeks. Sounds generous.
But here’s what actually happens: limits quietly drop, a temporary 2x makes the reduced limit feel normal, the promo ends, and you’re left at a baseline lower than where you started. You just didn’t notice the downgrade because the 2x absorbed the transition.
These AI plans are massively subsidized. The raw compute behind a heavy user costs multiples of the subscription price. Every move like this is the subsidy quietly correcting.
Very sneaky, Anthropic.
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