What problem does it solve?
Users often struggle to decide where to store newly created AI assets (skills, plugins, agents) — whether they belong in the shared forge-harness meta-skill library for cross-project use, as a project-local agent for team-specific workflows, or are not needed at all. This skill eliminates that guesswork with a structured, consistent evaluation process.
Core Features & Use Cases
- 4-Criteria FH Meta-Skill Evaluation: Checks if an asset has cross-project value, acts as an orchestration/judgment layer, can't be replaced by built-in tools, and has no overlap with existing FH skills.
- Project-Local Fallback Assessment: For assets that don't qualify as FH meta-skills, it evaluates if they have project-specific knowledge, are part of a repeated workflow, or offer local-context convenience.
- Duplicate Prevention Pre-Scan: Runs a mechanical grep check before LLM evaluation to catch verbatim name/trigger overlaps with existing skills, reducing false positives from duplicate assets.
- Use Case: If you build a new agent for your team's internal Jira ticket creation workflow, this skill will tell you if it's generic enough to share across all your projects as an FH skill, or if it's tied to your team's specific Jira setup so it should stay as a project-local agent.
Quick Start
Use the asset-placement-gate skill to evaluate where your new internal documentation summarization agent should be placed.
