discover-flow-verification
discover-flow-verification is a Test strategy & case design skill for AI agents, published by benjaminshoemaker in the ai_coding_project_base repository on GitHub. Skillspace rates discover-flow-verification D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Guide discovery and discussion to determine how an AI coding agent can autonomously run and verify a specific user flow end to end. Use when the user asks what would be needed to test a flow, prove an app works, create an E2E/self-test harness, make agent verification repeatable, or evaluate whether an existing test setup lets the agent verify behavior. This skill is for shaping the verification approach before implementation: read the repo's product docs and existing verification contracts, identify the real user flow and channel under test, uncover missing controllable state or external dependencies, ask focused questions, and produce a concrete agent-runnable verification plan only after the flow is understood.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 40
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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