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skills-vote

AI Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
83 / 100 · community maintained
Author / version / license
@MemTensor · no license declared
Token usage
Lean
Setup complexity
Guided setup
External API key
Required · GitHub
Operating systems
macOS · Linux · Windows
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
  • Shell exec
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。; 检出高风险片段：pipe_curl_to_shell

Output preview skills-vote.preview 
---
name: skills-vote
description: Find the most relevant external agent skills for the current task, then submit grounded feedback…
category: ai
runtime: no special runtime
---

# skills-vote output preview

## PART A: Task fit
- Use case: Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Preconditions / Recommend / Request schema” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.”.
- **02** When the source has headings, the agent prioritizes “Preconditions / Recommend / Request schema” 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; may access external network resources; requires GitHub API keys.

## Running Rules
- read files, write/modify files, run shell commands; may access external network resources; requires GitHub 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: Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actuall…
  • 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 “Preconditions”, “Recommend”, “Request schema”, “Example”, 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 skills-vote 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 “Preconditions / Recommend / Request schema” before expanding.

Boundaries And Review

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

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