agent-academy-stargazers

Name: agent-academy-stargazers
Author: microsoft

AI Community

Repo stars 2,528
Author repo agent-academy

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

Decide Fit First

Core job: > This skill fetches the list of users who have starred the microsoft/agent-academy repository on GitHub. If the user provides a…
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 “🏁 Prerequisites”, “🛠️ Skill Flow”, “📜 Rules”, 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-stargazers 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 “🏁 Prerequisites / 🛠️ Skill Flow / 📜 Rules” 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.

Agent Academy Stargazers

This skill fetches the list of users who have starred the microsoft/agent-academy repository on GitHub. If the user provides a GitHub username, it also checks whether that specific user has starred the repo.

🏁 Prerequisites

Before running, verify the gh CLI is installed and authenticated:

Run gh --version to confirm it is installed. If not, install it (e.g., brew install gh on macOS, or the appropriate package manager for the user's OS).
Run gh auth status to confirm it is authenticated. If not, run gh auth login and guide the user through the interactive authentication flow.

Do not proceed with the rest of the skill until both checks pass.

🛠️ Skill Flow

Verify the gh CLI is installed and authenticated (see Prerequisites above).
Fetch the full list of stargazers for the microsoft/agent-academy repository using gh api with the --paginate flag to automatically retrieve all pages (the API returns at most 100 results per page, so pagination is required for repos with more than 100 stargazers). Use --jq to select only the login, type, and url fields from each stargazer object.
Save the filtered list to temp/stargazers.json in the repository root.
Tell the user how many stargazers were found and that the list was saved to temp/stargazers.json.
If the user provided a GitHub username, check whether that username appears in the stargazer list and tell the user the result (e.g., "✅ @username has starred the repo" or "❌ @username has not starred the repo").

📜 Rules

Always use gh api --paginate to fetch stargazers for microsoft/agent-academy, ensuring all pages are retrieved (use per_page=100 for efficiency).
Always use --jq '[.[] | {login, type, url}]' to limit the response to only the login, type, and url fields. Do not store any other fields.
Always save the result to temp/stargazers.json relative to the repository root, creating the temp directory if it does not exist.
Always fetch fresh data from GitHub — never rely on previously fetched results or conversation history.
If the gh CLI is not installed, install it using the appropriate package manager for the user's OS (e.g., brew install gh on macOS) before proceeding.
If the gh CLI is not authenticated, run gh auth login and guide the user through authentication before proceeding.
If the API request fails (e.g., network error, repo not found), report the error clearly to the user and do not write a partial or empty file.

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: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: macOS
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-stargazers.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Agent Academy Stargazers

This skill fetches the list of users who have starred the `microsoft/agent-academy` repository on GitHub. If the user provides a GitHub username, it also checks whether that specific user has starred the repo.

## 🏁 Prerequisites

Before running, verify the `gh` CLI is installed and authenticated:

1. Run `gh --version` to confirm it is installed. If not, install it (e.g., `brew install gh` on macOS, or the appropriate package manager for the user's OS).
2. Run `gh auth status` to confirm it is authenticated. If not, run `gh auth login` and guide the user through the interactive authentication flow.

Do not proceed with the rest of the skill until both checks pass.

## 🛠️ Skill Flow

1. Verify the `gh` CLI is installed and authenticated (see Prerequisites above).
2. Fetch the full list of stargazers for the `microsoft/agent-academy` repository using `gh api` with the `--paginate` flag to automatically retrieve all pages (the API returns at most 100 results per page, so pagination is required for repos with more than 100 stargazers). Use `--jq` to select only the `login`, `type`, and `url` fields from each stargazer object.
3. Save the filtered list to `temp/stargazers.json` in the repository root.
4. Tell the user how many stargazers were found and that the list was saved to `temp/stargazers.json`.
5. If the user provided a GitHub username, check whether that username appears in the stargazer list and tell the user the result (e.g., "✅ @username has starred the repo" or "❌ @username has not starred the repo").

## 📜 Rules

- Always use `gh api --paginate` to fetch stargazers for `microsoft/agent-academy`, ensuring all pages are retrieved (use `per_page=100` for efficiency).

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> 🏁 Prerequisites → 🛠️ Skill Flow → 📜 Rules
terms    -> This skill fetches the list of users who have starred the microsoft/agent-academy repository on GitHub. · 1. Run gh --version to confirm it is installed. · Do not proceed with the rest of the skill until both checks pass. · 1. Verify the gh CLI is installed and authenticated (see Prerequisites above).
files/cmd -> microsoft/agent-academy · gh --version · brew install gh · gh auth status · gh auth login · gh api · --paginate · --jq
body sha256 -> 8096587ae20d