Explore SkillOpt for improving Cursor agent skills from our session data
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 28/100
Research direction
Start by reviewing SkillOpt's documented input and validation flow, then inspect the local AGENTS.md, .cursor/agents/, shared skills, and ~/.cursor/projects/*/agent-transcripts/ layout. Compare those inputs with the existing skill-creator eval loop. Done means a documented fit decision, a scoped Cursor transcript adapter proposal, and concrete evaluation, privacy, and cost criteria.
Written by the indexing model from the issue text.
Description
We use Cursor heavily on our system (and related repos) with AGENTS.md, .cursor/agents/, and shared skills.
Our session transcripts live locally under ~/.cursor/projects/*/agent-transcripts/.
Microsoft SkillOpt looks like a way to improve those skill documents from real usage — with validation so changes only land when they actually help.
Ask: Is this worth pursuing for our setup? SkillOpt doesn’t appear to support Cursor transcripts out of the box, so we’d need some adapter work.
Questions to answer:
- Does SkillOpt fit how we work, or is our existing
skill-creatoreval loop enough? - What would we optimize first —
AGENTS.md, PR review agents, or domain skills? - How would we know a skill change is actually better (tests, lint, PR review quality)?
- What’s the privacy/cost tradeoff of sending harvested session data to an optimizer API?
- Dominant language
- Python
- Stars
- 17.3k
- Forks
- 1.6k
- Avg merge
- 8d 16h
- Merged PRs (30d)
- 11
Getting set up
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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