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 kalyvask/winning-writing --skill performance-review-coachgit clone --depth 1 https://github.com/kalyvask/winning-writingWrote 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/kalyvask/winning-writing/performance-review-coach)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/performance-review-coach"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/performance-review-coach/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/kalyvask/winning-writing/performance-review-coach"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/performance-review-coach.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 | $0.00120 | $0.01710 |
| Opus 5 | $0.00060 | $0.00855 |
| Sonnet 5 | $0.00024 | $0.00342 |
| Haiku 4.5 | $0.00012 | $0.00171 |
Grade A, and why
performance-review-coach 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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance-review coach
Source: points/performance-review-rules.md and points/banned-jargon.md. Read those first.
What this skill does
Given a draft review (or a target person + context), produce a review that the recipient would save if their house were on fire. Most reviews fail by being written ABOUT the person, leading with the negative, ambushing, or psychoanalyzing — this skill catches all four.
The philosophy in one line
A review is a letter to one person, not a report card sent up to management. Read aloud, it should sound like you talking to them. If it reads like it's written to their mother, rewrite it.
Mode 1 — Draft from scratch
If the user gives you a person + context, walk through Kramon's seven rules in order before writing:
- Letter form — write to them, not about them. "You ably led..." not "Glenn ably led..."
- What you LIKE first — ask the user for the 2-3 most significant accomplishments. Cite colleagues if any said something specific. Never lead with the negative. Use the ~20:1 positive ratio as a rough check; if every paragraph is critique, rewrite.
- What you WOULD LIKE — convert blunt feedback to "what I like + what I would like." See the reframing table in
performance-review-rules.md. Phrase as a question where possible. - Near-term goals — specific, named, with what you will look for.
- Long-term goals including the next job — name the role they likely want next; name the 2-3 competencies they need to develop to get there. This is the move that earns the most loyalty.
- Colleague feedback — if you have it, fold it in directly ("your colleagues describe you as..."). It lands ten times harder than the boss's own praise.
- Vivid examples — every claim, positive or negative, anchored to a specific moment, project, or behavior. Vague reviews are the worst kind.
- Thank you — close with a specific thank-you. Two words minimum, ideally a paragraph.
Then write. Length target: 400 words for a peer or report; 600+ for a star performer where the long-term-job section is fully built out.
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.
- 11d ago First seen · 95 lines · 120 tokens per session scan A df3fa8f9f770
performance-review-coach is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed 5d ago), licensed MIT. It adds 120 tokens to every session and 1,710 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
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Generate a project-specific context file from a brief so an AI assistant remembers your editorial constraints, voice, audience, and quality bar across sessions.
project-retrospective
Generate a LESSONS.md from a finished project: what worked, what didn't, what to reuse, what to retire — formatted for next-project carry-over.
template-selector
Recommend the right skill bundle, agent, and workflow sequence for a new project — so media professionals can start producing instead of browsing a 394-skill library.
multi-author-harmonizer
Reviews a text written or assembled by multiple authors and produces a detailed inconsistency report — flagging voice shifts, terminology mismatches, tonal clashes, and formatting discrepancies — with specific harmonisation recommendations for each.
jargon-flagger
Scans a draft and flags every instance of technical jargon, unexplained acronyms, and insider language that a general-audience reader would not understand — with a plain-language alternative for each.
passive-voice-checker
Identifies every passive-voice construction in a draft, assesses whether each weakens or serves the prose, and offers active-voice alternatives for those that should be changed — while leaving justified passives alone.