The description names the domain (investor identification/evaluation for fundraising) and mentions some actions like 'identify, evaluate, or prioritize potential investors,' but doesn't list specific concrete outputs or actions (e.g., 'scores investors by fit,' 'generates ranked lists,' 'filters by stage/sector').

Clearly answers both 'what' (identify, evaluate, or prioritize potential investors for a fundraising round) and 'when' (explicit trigger phrases and activation conditions using 'Also activates when...').

Includes strong natural trigger terms users would actually say: 'who should I pitch?', 'find me investors', 'build an investor list', 'VC/angel targeting', and 'fundraising round'. These cover common variations well.

The description carves out a clear niche around investor targeting and fundraising. The specific trigger phrases like 'build an investor list' and 'VC/angel targeting' are unlikely to conflict with other skills.

The skill is fairly comprehensive but includes some content that Claude already knows (e.g., explaining what angels are, that fundraising is a funnel, basic CRM advice). The 'Common Mistakes to Avoid' and 'Angel Investor Considerations' sections contain general startup knowledge that adds token cost without much unique instructional value. However, the core frameworks (7-Point Filter, Tiering) are genuinely useful structured guidance.

The workflow steps are clearly described and the 7-point filter provides concrete criteria, but the skill lacks executable code or commands. The 'Build the raw list' step says 'Research investors matching the criteria' without specifying how Claude should actually do this (what tools to use, what searches to run). The output format table template is helpful but the actual research methodology is vague given Claude's tool constraints.

The 7-step workflow is clearly sequenced with logical dependencies (read context → define criteria → build list → check conflicts → score/tier → find warm paths → deliver). The conflict-checking step serves as a validation checkpoint, and the tiering framework provides clear decision criteria. The output format is well-specified with explicit sections for conflicts and research gaps, which serves as a verification mechanism.

The skill references related skills (pitch-deck, fundraising-email) and external context (startup-context.md), which is good. However, the content is quite long (~150 lines) with frameworks, best practices, common mistakes, and angel considerations all inline. The sourcing information section and common mistakes could be split into referenced files to keep the main skill leaner.