agent-skill-stack
Find, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills wo
Profile →npx skills add github/awesome-copilot --skill agent-skill-stack --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Build an Agent Skill Stack Build the smallest useful stack for the user's actual outcome. Never force a domain example or a fixed lifecycle onto a different request. ## 1. Choose the user-facing depth Default to **plain-language mode**. Assume the user does not need to understand paths, revisions, hashes, manifests, static analysis, or runtime details. In plain-language mode, show: - what the user is trying to accomplish; - the steps in everyday language; - which capabilities are already available; - which Skills are recommended, optional, overlapping, or unsuitable; - how widely each candidate is used; - whether it passed an installation safety check and a safe trial; - what account access or external actions it may require. Keep source paths, revisions, file fingerprints, raw scores, audit evidence, and dependency details in the internal record. Show them only when the user asks for technical details or when a specific technical fact is necessary for informed consent. ## 2. Derive the workflow dynamically Read [references/workflow-model.md](references/workflow-model.md). Begin with the final result the user wants, not the domain words in the request. Ask only questions whose an
- 1. Choose the user-facing depth
- 2. Derive the workflow dynamically
- 3. Search the local index first
- 4. Map capabilities, including indirect helpers
- 5. Search with four lenses
- 6. Verify and rank candidates
- 7. Analyze conflicts and scope
- 8. Present recommendations in plain language
- 9. Install only after consent
- 10. Run a recall check
python3 scripts/skill_index.py build \ python3 scripts/render_stack_card.py \ python3 scripts/stage_install.py \ python3 scripts/project_profile.py \
What does the agent-skill-stack skill do?
Find, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills wo
How do I install it?
Run `npx skills add github/awesome-copilot --skill agent-skill-stack --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.