performance-review-writer
Skill0 starsChanged 38 days agoMITView on GitHub
Writes fair, specific, evidence-based performance reviews and ratings. Use when drafting or preparing for calibration to reduce recency bias and vague praise or criticism.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- github/awesome-copilottheir own version, first seen Oct 25, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- github/awesome-copilot★ 41ksame name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
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- eng-status-rollupSkill
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 34
Every evaluative claim is backed by a cited, specific example - no unevidenced adjectives survive.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Performance Review Writer
Performance reviews are legally significant, career-defining, and often written in 20 minutes from memory. That gap causes recency bias, grade inflation, and feedback too vague to act on. This skill closes it.
Before Writing: Gather Evidence First
Collect raw material from four sources before writing a word: the employee's self-review, peer feedback verbatims, the role's defined expectations for their level, and a concrete list of their work from the review period (PRs merged, projects owned, incidents handled, cross-functional contributions). Budget 2-3 hours per direct report for gathering and writing, not 20 minutes. Do not write from memory. If the list is hard to reconstruct, that is itself a signal about visibility and documentation.
Rating Calibration
Start from the role expectations document, not a bell curve. Ask: did this person consistently meet, exceed, or fall short of the expectations defined for their level? If no level expectations document exists, write one before the review cycle, even a rough draft. Ratings applied without a standard are arbitrary. Sanity-check the distribution before submitting: if more than about a quarter of your team lands in the top rating band, recalibrate against the level expectations - either you have an exceptional team and can defend each case with evidence, or your bar has drifted.
Writing the Narrative
Open with a one-sentence summary of the overall rating and why. Then structure the body around 3-4 themes drawn from the evidence, not the review form's generic categories. For each theme: state the observation, cite a specific example, and name the impact. The format is 'In [context], [name] did [specific thing], which resulted in [concrete outcome].' A full narrative typically lands at 400-800 words - shorter usually means missing evidence, longer usually means unprioritized themes. Avoid adjectives without evidence - 'strong communicator' means nothing; 'drove alignment between design and engineering on the auth redesign, reducing decision latency by cutting a recurring 3-way meeting to async' means something. Spread the cited examples across the whole review period; if every example comes from the last 6 weeks, the review is documenting your recency bias, not their year.
Development Section
Every review includes one primary growth area and one specific suggested action for the next review period. Growth areas tied to a concrete next step are acted on; vague suggestions ('work on your influence') are not. Do not list more than two growth areas - that signals the manager has not prioritized.
Deliverable
Produce a complete review packet containing: the one-sentence rating summary, the 3-4 theme narrative with a cited example and named impact per theme, one primary growth area paired with a specific action for the next period, and the evidence list used (work items, peer verbatims, level expectations referenced) so the rating survives calibration questioning.
Quality bar
- Every evaluative claim is backed by a cited, specific example - no unevidenced adjectives survive.
- Examples span the full review period, not just the final 6 weeks.
- The rating traces directly to the level expectations document, not to a curve or a gut feel.
- The growth area is specific enough that the employee could start acting on it tomorrow without asking what it means.
- The review contains nothing the employee is hearing for the first time - surprises belong in earlier 1:1s, not the written record.
Common Mistakes to Avoid
Halo effect: one visible win or loss skewing the whole rating. Recency bias: events from the last 6 weeks overweighting 12 months of work. Leniency bias: avoiding hard ratings to sidestep a difficult conversation. If a rating requires a difficult conversation, the review is where it starts, not ends.
Does it work?
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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