fluxly Search skills… ⌘K 中文 Publish

agilab-runbook

Other Verified
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Other
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
94 / 100 · audit passed
Author / version / license
@ThalesGroup · BSD-3-Clause (see repo LICENSE)
Token usage
Heavy
Setup complexity
Manual integration
External API key
Required · OpenAI
Operating systems
macOS · Linux · Windows
Runtime requirements
Node.js · Python
Permissions
  • Read-only
  • Write / modify
  • Shell exec
  • Env read
Network behavior
External requests
Install commands
26 variants

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 agilab-runbook.preview 
---
name: agilab-runbook
description: Runbook for working in the AGILab repo (uv, Streamlit, run configs, packaging, troubleshooting).…
category: other
runtime: Node.js / Python
---

# agilab-runbook output preview

## PART A: Task fit
- Use case: Runbook for working in the AGILab repo (uv, Streamlit, run configs, packaging, troubleshooting). Use this skill when you need repo-specific “how we do things” guidance in agilab/: launching Streamlit, regenerating run-config wrappers, debugging installs, or preparing releases. requires OpenAI API key; runs on Node.js. Works with Claude Code, Cursor, Cline….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “AGILab working rules (repo policy) / Git footprint maintenance / Common commands (from the runbook matrix)” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Runbook for working in the AGILab repo (uv, Streamlit, run configs, packaging, troubleshooting). Use this skill when you need repo-specific “how we do things” guidance in agilab/: launching Streamlit, regenerating run-config wrappers, debugging installs, or preparing releases. requires OpenAI API key; runs on Node.js. Works with Claude Code, Cursor, Cline…”.
- **02** When the source has headings, the agent prioritizes “AGILab working rules (repo policy) / Git footprint maintenance / Common commands (from the runbook matrix)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires OpenAI API keys.

## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires OpenAI API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Runbook for working in the AGILab repo (uv, Streamlit, run configs, packaging, troubleshooting). Use this skill when you need re…
  • 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 “AGILab working rules (repo policy)”, “Git footprint maintenance”, “Common commands (from the runbook matrix)”, “CI and badge checks”, 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 agilab-runbook 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 “AGILab working rules (repo policy) / Git footprint maintenance / Common commands (from the runbook matrix)” before expanding.

Boundaries And Review

  • Dependencies: Prepare OpenAI API keys before running a full task.
  • Permissions: Declared permissions include read / write / shell-exec / env-read; 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.

Discussion

Powered by GitHub Discussions. Sign in with GitHub to comment, react, or subscribe.