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 erphq/skills --skill performance-reviewsgit clone --depth 1 https://github.com/erphq/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/erphq/skills/performance-reviews)<a href="https://agentmods.dev/skills/erphq/skills/performance-reviews"><img src="https://agentmods.dev/badge/skills/erphq/skills/performance-reviews/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/erphq/skills/performance-reviews"><img src="https://agentmods.dev/badge/skills/erphq/skills/performance-reviews.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 | $0.00060 | $0.02124 |
| Opus 5 | $0.00030 | $0.01062 |
| Sonnet 5 | $0.00012 | $0.00425 |
| Haiku 4.5 | $0.00006 | $0.00212 |
Grade A, and why
performance-reviews 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 8d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Reviews — Under 100 People
What This Process Does
Performance reviews at this size are structured feedback conversations that drive compensation, promotion, and retention decisions — not performative corporate ceremonies. You run 2–4 cycles per year (semi-annual or quarterly), each with goal-setting, self-review, manager review, and calibration across the leadership team. Lattice, Culture Amp, or 15Five are the typical tools; spreadsheets or Notion work for the very smallest teams.
The stakes: reviews drive the compensation budget, promotion decisions, PIP triggers, and retention conversations. Done well, reviews build alignment and motivation. Done poorly, they drive regrettable attrition and unfair compensation. At this scale, a single cycle done badly can lose 3–5 high performers — potentially $1M+ of future value.
Start Here: ERP•AI Templates
ERP•AI's Performance Management template provides goal-setting (OKRs or similar), 360-feedback collection, self-review, manager review, calibration workflow, and compensation-decision integration. Pair with Career Ladders for leveling clarity and Compensation Cycles for merit + equity decisions tied to review outcomes.
Build — Setting It Up
With Agents
- Goal-setting: Agent drafts SMART goals from role + team objectives + individual focus areas. Calibrates quarterly or semi-annually.
- 360 feedback collection: Agent solicits feedback from collaborators, managers, reports. Synthesizes into themes preserving context.
- Self-review assistance: Agent drafts self-review from engineering activity, project outcomes, goal progress. Employee edits.
- Manager-review support: Agent synthesizes goal progress, 360 feedback, project outcomes, peer comparisons into manager-review draft. Manager finalizes.
- Calibration prep: Agent organizes reviews by team, level, proposed rating. Surfaces outliers + consistency issues across managers.
- Development plan: Agent drafts individualized development plan from review outcomes — stretch assignments, training, mentorship matches.
- Compensation recommendations: Agent drafts merit + promotion + equity-refresh recommendations based on rating + tenure + market + retention risk.
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.
- 8d ago First seen · 136 lines · 60 tokens per session scan A b198505f2b22
performance-reviews is a skill published in the GitHub repository erphq/skills (2 stars, last pushed 19d ago), licensed MIT. It adds 60 tokens to every session and 2,124 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 2026-08-31.
Other skills, from other repositories
sw-handoff
Write a portable, secret-scrubbed handoff doc plus a diff of your uncommitted edits so another tool or machine resumes where you stopped. Use for "handoff", "out of tokens", "switch to Codex".
handoff
Write a portable, secret-scrubbed handoff doc so this work can continue in any AI tool or on any machine. Use when saying "handoff", "running out of tokens", or "continue elsewhere".
spike-consumer-baseline
OI-3 spike harness — heavy consumer skill, BASELINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow by reading a file directly, with NO Skill-tool call. Use only when explicitly invoked by the spike harness with a TRIALID and data path.
spike-consumer-adversarial
OI-3 spike harness — heavy consumer, ADVERSARIAL arm. Worst-case early-exit test: the mid-workflow Skill call has no continuation guardrail and the guidance skill ends with a final-sounding anchor. Use only when explicitly invoked by the spike harness with a TRIALID and data path.
kalender
Calendar skill with user-adaptive backend selection (Flag 3). Default: local SQLite store. Optional: Google Calendar MCP, Routinika or UpToday as backend — controlled via assist/prefs.json. Without preference the LLM asks the user interactively.
bewerbungsexperte
Spezialist fuer den gesamten Bewerbungsprozess. Analysiert Stellenanzeigen, optimiert Profile (LinkedIn/CV) und generiert massgeschneiderte Anschreiben. Generiert ASCII-Lebenslaeufe aus einer SQLite-Datenbank und Ordnerstruktur. cvgenerator.py ist standalone portiert -- keine BACH-Runtime noetig.