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 jeffreytse/grimoire-core --skill design-performance-review-systemgit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/design-performance-review-system)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/design-performance-review-system"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/design-performance-review-system/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/jeffreytse/grimoire-core/design-performance-review-system"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/design-performance-review-system.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 | $0.00026 | $0.00793 |
| Opus 5 | $0.00013 | $0.00396 |
| Sonnet 5 | $0.00005 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
design-performance-review-system 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Performance Review System
Build a structured performance evaluation framework that drives development and fair assessment.
Why This Is Best Practice
Adopted by: Deloitte, Google, Adobe, Microsoft, GE (post-stack-rank), Netflix Impact: Deloitte found 58% of executives said traditional reviews were poor use of time; after redesign saw 14% improvement in employee engagement scores. Adobe eliminated annual reviews in 2012 and cut voluntary turnover by 30%. Why best: Frequent, forward-looking feedback addresses performance in real time rather than via retrospective annual surprises. Separating development conversations from compensation decisions reduces bias and defensiveness.
Sources: Buckingham & Goodall "Reinventing Performance Management" HBR (2015); Google re:Work research; SHRM Performance Management Guidelines (2023)
Steps
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Audit current state — survey managers and employees on pain points with existing process; quantify time cost (hours × headcount × frequency).
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Choose cadence — select review frequency: continuous check-ins (weekly/biweekly), quarterly snapshots, and annual calibration. Match cadence to business velocity.
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Define rating dimensions — limit to 3–5 competencies per role level (e.g., impact, collaboration, growth, execution). Avoid generic trait ratings like "attitude."
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Design the rating scale — use 4- or 5-point behaviorally anchored scales with written descriptors for each level. Eliminate middle-point ambiguity.
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Separate development from compensation — run development reviews (manager + employee focused on growth) at different times than calibration sessions that determine pay and promotion.
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Build calibration process — convene cross-functional calibration panels to normalize ratings across teams and reduce individual manager bias.
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Create manager training — train all people managers on giving SBI (Situation-Behavior-Impact) feedback, conducting review conversations, and avoiding common biases (recency, halo, affinity).
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 · 63 lines · 26 tokens per session scan A cda73f423e5d
design-performance-review-system is a skill published in the GitHub repository jeffreytse/grimoire-core (4 stars, last pushed 23d ago), licensed MIT. It adds 26 tokens to every session and 793 once invoked, about $0.0001 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.
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