Work to AI Execution Manual
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About this skill
Problem to solve
Teams often finish a project, fix a workflow, or collect hard-won lessons, but that knowledge remains in chat logs, verbal notes, and scattered docs. An agent may still ask 'what next', repeat the same mistake, or fail to verify completion. This skill focuses on turning completed work into a reproducible execution manual instead of writing a vague document first.
How it works
- Choose the path: separate
new skill creationfromupdating an existing skillto avoid overwriting working content. - Follow the stages: identify fit, extract experience, record teaching, package files, validate reproducibility, and desensitize data.
- Make gotchas executable: write 'if X then Y', not 'watch out for this trap'.
- Keep structure tight: keep
SKILL.mdas a clear entry point, move detail beyond about 30 lines intoreferences/, and include completion checks. - Handle updates and privacy: apply incremental updates with
SemVer, and desensitize eight sensitive categories when selected by the user.
Boundaries
Best for completed, repeatable work with clear validation. Not suited for one-off tasks, generic tools, or project-only conventions. The skill should come from real failure records and teaching sessions, not theory alone; if it contains internal information, desensitization must be confirmed before delivery.
Use Cases
- Turn a validated approval workflow into roles and steps so an agent can handle routing independently.
- Extract boundaries from real AI error records and write executable if-X-then-Y rules.
- Add a new gotcha to an existing skill with SemVer update and CHANGELOG.
- Before sharing, scan for API keys, org names, and approval paths, then desensitize selected items.
Best For
- product managers who need to distill finished project workflows into agent-ready manuals
- engineers maintaining an agent skill library and enforcing packaging standards
- project leads sharing internal workflows externally while desensitizing sensitive data
- operations staff turning repeated collaboration pitfalls into reusable skill packages
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