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.
git clone --depth 1 https://github.com/latestaiagents/agent-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/commands/latestaiagents/agent-skills/performance-review)<a href="https://agentmods.dev/commands/latestaiagents/agent-skills/performance-review"><img src="https://agentmods.dev/badge/commands/latestaiagents/agent-skills/performance-review.svg" alt="Measured on agentmods" 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 | $0.00008 | $0.00329 |
| Opus 5 | $0.00004 | $0.00164 |
| Sonnet 5 | $0.00002 | $0.00066 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
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 3d 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.
What it actually says
/performance-review
Write effective performance reviews that drive growth.
What I Need
Tell me:
- Employee name and role
- Review period
- Key accomplishments (3-5)
- Areas of strength
- Areas for development
- Overall performance level
- Goals for next period
What I'll Create
A complete performance review with:
- Executive summary - Overall performance snapshot
- Accomplishments - Specific achievements with impact
- Strengths - What they do well with examples
- Development areas - Constructive feedback with suggestions
- Goals - SMART goals for next period
- Rating - According to your scale
Feedback Quality
I use the SBI model for all feedback:
- Situation - When/where it happened
- Behavior - What they specifically did
- Impact - The result of their action
Example
You: /performance-review
Employee: Sarah Chen, Software Engineer
Period: Q1-Q4 2025
Accomplishments: Led API migration, mentored 2 interns, reduced deploy time by 40%
Strengths: Technical depth, collaboration
Development: Documentation, stakeholder communication
Level: Exceeds expectations
Claude: [Generates complete review with specific, actionable feedback]
Avoid
I'll help you avoid:
- Vague feedback ("good job")
- Recency bias (focusing only on recent events)
- Halo/horn effects
- Discriminatory language
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.
- 3d ago First seen · 60 lines · 8 tokens per session scan A 25cf8585666b
performance-review is a command published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 8 tokens to every session and 329 once invoked, about $0.0000 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.
Other commands, from other repositories
fdk-react-create
Create a new Platform 3.0 React Meta app (default UI stack). Uses fdk create react-starter-template or react-meta skeletons with DEW components, metaConfig in manifest.json, and React Router.
fdk-refactor
Reduce function complexity in a Freshworks app to meet cyclomatic complexity ≤ 7 per function. Extracts helper functions, simplifies conditionals, and preserves behavior while improving code quality.
fw-setup-uninstall
Uninstall FDK completely — keeps Node.js and nvm (/fw-setup uninstall).
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.