PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 skills add mohitagw15856/pm-claude-skills --skill mcp-server-specgit clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsWrote 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/skills/mohitagw15856/pm-claude-skills/mcp-server-spec)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/mcp-server-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/mcp-server-spec/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/skills/mohitagw15856/pm-claude-skills/mcp-server-spec"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/mcp-server-spec.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 | $0.00101 | $0.01277 |
| Opus 5.5 | $0.00040 | $0.00511 |
| Sonnet 5.5 | $0.00020 | $0.00255 |
| Haiku 4.5 | $0.00010 | $0.00128 |
Grade A, and why
mcp-server-spec 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 2d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Server Spec Skill
Every SaaS is shipping an MCP server; most dump their REST API as forty tools and wonder why agents flail. This skill designs the server as what it actually is: a user interface for a non-human user — few tools, task-shaped, with descriptions written for a model deciding under uncertainty.
What This Skill Produces
- A toolset design: 3-10 tools mapped to agent tasks, not API endpoints
- Per-tool specs: name, description (the routing surface), parameters, returns, error behaviour
- Auth & scoping decisions: how credentials flow, what a token can never do
- An explicit not-exposed list with reasons — the most load-bearing section
- A test plan: the agent-eval loop that proves the toolset works
Required Inputs
Ask for (if not already provided):
- The product and what users hire it for (the top 5 jobs, not the feature list)
- The existing API surface (endpoints or capability list) if one exists
- Who the agent acts for — the end user's own account? a service account? multi-tenant?
- The riskiest actions the product supports (deletes, sends, payments, permission changes)
Design Method
- Start from agent tasks, not endpoints. List the 5-8 things an agent will actually be asked to do with this product ("file an expense", "find last quarter's report", "summarise ticket history"). Each becomes one tool — even if it spans four API calls internally. An endpoint-mirrored toolset makes the agent do your orchestration; a task-shaped one does it for them.
- Keep the toolset small. Every tool dilutes selection accuracy on every call. Target ≤10; past ~15, split into separately-loadable servers by workflow. Merge list/get/search variants behind one tool with parameters where natural.
- Write descriptions as routing surfaces. The description is all the model sees when choosing. Formula per tool: what it does (one clause) · when to use it and when to use the sibling tool instead · what it returns. Test: could a model pick correctly between your two closest tools from descriptions alone?
- Design returns for context windows. Return the 6 fields an agent needs, not the 60 the API has; include stable IDs for chaining; paginate with explicit
has_more; keep any response under ~2k tokens by default with an opt-in for detail. - Make errors instructive. An agent retries what it understands:
"date must be YYYY-MM-DD"beats400 Bad Request. Every error names the parameter at fault and the fix. - Draw the safety boundary. Classify every capability: expose (read/create, low blast radius) · expose gated (destructive/outward-facing — require an explicit confirmation parameter and document that clients should surface approval) · never expose (auth changes, deletes without recovery, bulk exports of other users' data). The never-list ships in the spec with reasons.
- Specify auth honestly. OAuth per end user (agent acts as the user, inherits their permissions) vs API key (service account — then per-tool scoping matters more). State token lifetime, revocation, and what happens mid-session on expiry.
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.
- 2d ago First seen · 78 lines · 101 tokens per session scan A 016fc1b2a3d3
mcp-server-spec is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 1,277 once invoked, about $0.0004 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 2026-10-12.
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