Reframe work from output/activity language to outcome language, on two surfaces — backlog items (Jira Epics and the like, classified on the Input→Activity→Output→Outcome→Impact taxonomy) and leadership asks (the drive-by directive that names a solution instead of a problem). Use when reviewing backlog epics in bulk, facilitating PI planning, running a value orientation audit, coaching a product owner on outcome-driven planning, or pressure-testing a message before a leader sends it. For whether an initiative is ready to commit rather than how it is worded, use sniff-test. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.
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tessl review fix ./skills/outcome-framing-coach/SKILL.mdThe skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The workflow optionally invokes a Python script (`scripts/prioritize_issues.py`) to read issues from a local GitHub repository workspace, which represents outsider-authored free text.
yyeret/ai-impact
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