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 ai-assisted-performance-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/ai-assisted-performance-review)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/ai-assisted-performance-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-assisted-performance-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/ai-assisted-performance-review"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-assisted-performance-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 | $0.00122 | $0.01329 |
| Opus 5.5 | $0.00049 | $0.00532 |
| Sonnet 5.5 | $0.00024 | $0.00266 |
| Haiku 4.5 | $0.00012 | $0.00133 |
Grade A, and why
ai-assisted-performance-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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Assisted Performance Review Skill
The uncomfortable review question of the decade: when a report ships twice the output with AI, what did they do? Volume stopped measuring effort; polish stopped measuring skill. Punishing AI use is as wrong as crediting the model's work to the human. This skill separates the signals — and gives managers the conversation, not just the theory.
What This Skill Produces
- A what-measures-whom analysis of the role's current evaluation criteria
- Rewritten criteria that measure the human: judgment, verification, outcomes, leverage
- Calibration rules for teams with uneven AI adoption
- Conversation scripts for the three hard cases
Required Inputs
Ask for (if not already provided):
- The role and current review criteria (the rubric, or how it really works)
- How AI shows up in the work — which tasks, how much of the output it drafts, what the tooling reality is
- The specific situation, if any: one person's review? team calibration? criteria rewrite?
- The org's AI stance — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour)
Method
- Sort every criterion: human, tool, or hybrid. Walk the current rubric. Volume of drafts, formatting quality, speed to first version → now mostly tool signals (evaluating them evaluates prompt luck and subscription tier). Decision quality, stakeholder trust, error catch rate, what they chose to build → still human. Output quality overall → hybrid: credit belongs to the pair, and the review's job is to see the human's contribution inside it.
- Rewrite around the four durable human signals:
- Judgment — what they decided to do, what they declined, how they scoped; the quality of taste applied to AI output (what they kept, cut, and corrected)
- Verification — do errors get caught before shipping? A person whose AI-assisted work is reliably right is demonstrating skill; one who forwards unverified fluency is a risk wearing productivity's clothes
- Outcomes — did the work move what it was for (the metric, the decision, the customer), independent of how it was produced
- Leverage — do they make AI multiply the team (shared prompts, workflows, teaching) or only their own count
- Set the calibration rules for mixed adoption. In one team you'll have a 2×-output adopter and a careful non-adopter. Rules that keep it fair: evaluate against the role's outcomes, not each other's volume · where AI use is sanctioned, not adopting is a development conversation (not a values one) · where someone's edge is invisible verification labour, surface it explicitly before comparing. Never let the review become a proxy war about the tools.
- Demand evidence that sees the human. Volume anecdotes are out. In: a sample of shipped work walked backwards (what did the AI draft, what did you change, why) · error/rework history · decisions log · peer signals about trust and leverage. The walk-backwards exercise is the single highest-signal artifact — put it in the review prep.
- Script the three hard cases:
- The volume star with thin judgment — "Your output doubled; let's walk three pieces backwards" (the conversation is about the delta between draft and shipped)
- The careful sceptic being out-shipped — outcomes-first framing; adoption raised as growth, not deficiency; their verification strength named as a strength
- The launderer — unverified AI work shipped as their own, errors reaching others: this is a reliability conversation with the accountability rule from the org's AI policy, not an AI conversation
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 · 122 tokens per session scan A dceb5d946eed
ai-assisted-performance-review is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 122 tokens to every session and 1,329 once invoked, about $0.0005 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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