agent-academy-mission-builder

AI Community
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: > This skill scaffolds new content for the microsoft/agent-academy repository at https://github.com/microsoft/agent-academy. It…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Phase 0: Gather Intel (Interview the User)”, “Phase 1: Reconnaissance (Repo Check)”, “Phase 2: Mission Naming”, “Phase 3: Scaffold the Markdown”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name agent-academy-mission-builder directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Phase 0: Gather Intel (Interview the User) / Phase 1: Reconnaissance (Repo Check) / Phase 2: Mission Naming” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.
Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates
Fluxly category
AI
Author-declared agents
No explicit declaration found; this is not inferred or tested compatibility
Static check
88 / 100 · heuristic scan, not runtime safety proof
Author / version / license
@microsoft · no license declared
Fluxly token estimate
Moderate
Fluxly setup estimate
Guided setup
External API key
No requirement detected
Detected OS requirements
Unspecified
Runtime requirements
Unspecified
Detected file/system behavior
  • Read-only
  • Write / modify
Detected network behavior
External requests
Install commands
None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools,默认拥有全部工具权限。

Output preview agent-academy-mission-builder.preview
# Phase 7: Deliver Output

1. **Scaffolded markdown file** create the mission file directly in the repo in the corresponding folder. If it's a recruit course mission, create a folder under `docs/recruit/` with the appropriate mission name and place an `index.md` file inside as well as an `assets` folder for images, for example, `docs/recruit/mcp-lab/index.md`. Repeat this pattern for operative and commander missions under their respective folders. For Special Ops, create the folder under `docs/special-ops/` with the slug name and place the `index.md` file and `assets` folder inside, for example, `docs/special-ops/mcp-lab/index.md`.
2. **Summary** of what was pre-filled vs. what needs manual work (be explicit about TODOs)
3. **Cross-link map** of which missions link here, and where this links
4. **For Special Ops**: Tags, difficulty, badge banner name, and image generation prompt
5. **Duplicate check result** with links to anything similar that exists

Discussion

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