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 context-engineering-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/context-engineering-review)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/context-engineering-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/context-engineering-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/context-engineering-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/context-engineering-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 | $0.00111 | $0.01224 |
| Opus 5.5 | $0.00044 | $0.00490 |
| Sonnet 5.5 | $0.00022 | $0.00245 |
| Haiku 4.5 | $0.00011 | $0.00122 |
Grade A, and why
context-engineering-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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering Review Skill
Most agent failures aren't model failures — they're context failures: instructions buried under retrieval dumps, stale history contradicting fresh facts, twelve tool definitions the task never needed. This skill audits the assembled window, not just the prompt text.
What This Skill Produces
- A context inventory: every component in the window, its size, and who put it there
- A keep / cut / restructure verdict per component, with the reasoning
- Ordering and cache-alignment fixes (stable prefix first, volatile content last)
- A token budget per component with an enforcement point
Required Inputs
Ask for (if not already provided):
- A real assembled context — an actual logged request (system prompt + messages + tools), not the template. If only the template exists, review that but flag that dynamic bloat is invisible
- The failure or goal — ignoring instructions? too expensive? inconsistent? slow?
- What varies per request (retrieval, history, user data) vs. what is static
- The model and its context limit, and current typical request size
Review Method
1. Inventory. List every component in window order: system prompt sections, tool definitions, retrieved documents, conversation history, few-shot examples, injected state. For each: token count (estimate if unlogged), static vs. dynamic, and owner.
2. Interrogate each component:
- Earning its tokens? Would removing it change outputs on real traffic? The honest test is ablation, not intuition.
- Right form? Raw dumps (full HTML, whole files, unabridged history) almost always beat down to summaries, excerpts, or references the agent can expand via a tool.
- Right position? Instructions that must win go in the system prompt; volatile data goes late; nothing critical hides in the middle of a long window.
- Fighting anything? Contradictions between sections (persona says terse, examples are verbose; old history asserts what retrieval now refutes) are the classic "ignores instructions" root cause.
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 · 85 lines · 111 tokens per session scan A 34b95641273c
context-engineering-review is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 111 tokens to every session and 1,224 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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