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 agent-design-reviewgit 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/agent-design-review)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/agent-design-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-design-review/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/agent-design-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/agent-design-review.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 | $0.00091 | $0.01060 |
| Opus 5.5 | $0.00036 | $0.00424 |
| Sonnet 5.5 | $0.00018 | $0.00212 |
| Haiku 4.5 | $0.00009 | $0.00106 |
Grade A, and why
agent-design-review 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Design Review Skill
Most agents don't fail because the model is weak — they fail because the design lets them loop, call the
wrong tool, lose the thread across steps, or burn tokens with no stopping rule. This skill reviews an agent's
architecture against the decisions that actually determine reliability, and ranks the fixes — so "it works in
the demo but not in prod" becomes a specific list of changes. (Writing a new agent spec? Use
agent-spec.)
Working from a brief
Given a sketch ("a research agent that searches, reads, and writes a report"), deliver the full review anyway — infer the likely control flow and tools, label the inference, and flag what to confirm. Never withhold the review for missing detail.
Required Inputs
Ask for these only if they aren't already provided (else infer and label):
- What the agent does — its goal, and what a successful run produces.
- Control flow — single prompt, plan-then-execute, ReAct loop, or multi-agent; and the stopping condition.
- Tools & actions — what it can call, and which actions have side effects (write, send, pay).
- Memory & context — what state carries across steps, and how context is kept in budget.
- Constraints — latency, cost per run, and the trust boundary (untrusted input? real-world actions?).
Output Format
Agent Review: [agent]
1. Summary — will this be reliable in production? The top 3 risks and the single change that helps most.
2. Findings by dimension — for each, what's sound and what's fragile:
| Dimension | Finding | Severity | Fix |
|---|---|---|---|
| Control flow | no max-steps / no progress check → loops | High | step budget + "am I making progress?" check + halt |
| Tool use | overlapping tools confuse selection | Med | fewer, sharply-described tools; allowlist |
| Context | full history re-sent each step → cost + drift | High | summarise/scope memory per step |
| Failure handling | one tool error aborts the run | Med | retry/backoff + graceful degradation |
| Safety | acts without confirmation on writes | High | human/confirm gate on side-effecting actions |
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 · 83 lines · 91 tokens per session scan A 9e0a4a44d692
agent-design-review is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 1,060 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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