self-review

self-review is a cursor rule for Cursor from mohitagw15856/pm-claude-skills. It costs 70 tokens per session (723 once invoked), scanned A, original, MIT.

A structured performance self-review that connects your accomplishments to their results, your workplace competencies, and your areas for growth.

In plain words
What is it for?
Use it to write a self-assessment for a performance cycle, organize evidence from a brag document, describe strengths and growth areas, and set goals.
Why use it?
It avoids a vague list of tasks and gives reviewers a clear account of your impact, limits, and next goals.

Cursor rule for Cursor

Written for Cursor: a Cursor rule (.mdc).

Good fit Use it to write a self-assessment for a performance cycle, organize evidence from a brag document, describe strengths and growth areas, and set goals.

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/mohitagw15856/pm-claude-skills/self-review
About the project

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.

mohitagw15856/pm-claude-skills · 1,352 stars · on GitHub · mohitagw15856.github.io

Install

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.

Clone the repo
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills

Made for: Cursor.

Wrote 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.

agentmods badge for self-review

README.md
[![agentmods](https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/self-review/github.svg)](https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/self-review)
Your own site
<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/self-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/self-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.

agentmods 80×15 button for self-review

Your own site · 80×15
<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/self-review"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 723 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00070 $0.00723
Opus 5 $0.00035 $0.00362
Sonnet 5 $0.00014 $0.00145
Haiku 4.5 $0.00007 $0.00072

Measured 7d ago against content hash 057f0da8d9de, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

self-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 7d 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.

exports/cursor/pm-career/self-review/self-review.mdc · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Self-Review Skill

A self-review is your one chance to frame your own year before someone else does. Done badly it's a vague list of activities; done well it's an evidenced narrative that maps your work to the competencies you're measured on, owns growth honestly, and sets up the next level. This skill writes that — pulling straight from a brag-doc if you have one.

Required Inputs

Ask for these only if they aren't already provided:

  • Your role, level, and the review period.
  • Accomplishments — your wins with impact/metrics (or point to a brag doc).
  • The competency framework / rating dimensions you're assessed on (if any).
  • Growth areas — where you fell short or want to develop (be honest; reviewers trust self-awareness).
  • Goals for the next period.

Output Format

Self-Review — [name], [role], [period]

1. Summary — 3–4 sentences: the headline of your period and the through-line. Lead with impact.

2. Key accomplishments — your top 3–6, each as outcome → your contribution → evidence → which competency it demonstrates. Quantify; tie to team/company goals.

3. Strengths — the 2–3 competencies you most demonstrated, with the proof.

4. Growth areas — 1–3, owned plainly: what was hard, what you learned, what you're changing. This section builds credibility when it's specific and non-defensive (not "I work too hard").

5. Goals & development plan — what you'll focus on next period and the support you need.

6. Rating rationale (if self-rating) — the rating you'd give and the evidence for it, calibrated to the framework — not inflated, not falsely modest.

Quality Checks

  • Accomplishments are quantified and tied to the competency framework / company goals
  • Each claim is backed by specific evidence, not adjectives
  • Growth areas are genuine and specific (not humble-brags), with what you're doing about them
  • The narrative has a through-line, not just a list
  • A self-rating (if used) is calibrated to the rubric with evidence — defensible, not aspirational

Read the full file on GitHub · 59 lines

Changes

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.

  1. 7d ago First seen · 59 lines · 70 tokens per session scan A 057f0da8d9de

Subscribe to this mod's changes

self-review is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 70 tokens to every session and 723 once invoked, about $0.0003 per session on Opus 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 2026-09-03.