outcome-engineering
outcome-engineering is a Structured reasoning & decision frameworks skill for AI agents, published by sam-dumont in the claude-skills repository on GitHub. Skillspace rates outcome-engineering D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Use when starting significant features, evaluating build-vs-buy decisions, reframing failed approaches, or when the user mentions "outcome engineering", "o16g", "outcome-first", "what's the outcome", or "frame this as an outcome". Also use proactively when a task description focuses on implementation ("build X", "add Y") without defining the desired outcome or verification criteria.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 38
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
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.