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Engineering Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Engineering
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@AgriciDaniel · no license declared
Token usage
Lean
Setup complexity
Guided setup
External API key
Not required
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
No special requirements
Permissions
  • Read-only
  • Write / modify
  • Shell exec
Network behavior
Local-only
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 ads-linkedin.preview 
---
name: ads-linkedin
description: LinkedIn Ads deep analysis for B2B advertising. Evaluates 27 checks across technical setup, audi…
category: engineering
runtime: no special runtime
---

# ads-linkedin output preview

## PART A: Task fit
- Use case: LinkedIn Ads deep analysis for B2B advertising. Evaluates 27 checks across technical setup, audience targeting, creative quality, lead gen forms, and bidding strategy. Includes Thought Leader Ads, ABM, predictive audiences, and the Oct 2025 Campaign Groups→Campaigns→Ad Sets terminology change. Use when user says LinkedIn Ads, B2B ads, sponsored content, lead gen forms, InMail, ABM ads, Thought Leader Ads, predictive audiences, B2B paid, or LinkedIn campaign..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Process / What to Analyze / Technical Setup (25% weight)” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “LinkedIn Ads deep analysis for B2B advertising. Evaluates 27 checks across technical setup, audience targeting, creative quality, lead gen forms, and bidding strategy. Includes Thought Leader Ads, ABM, predictive audiences, and the Oct 2025 Campaign Groups→Campaigns→Ad Sets terminology change. Use when user says LinkedIn Ads, B2B ads, sponsored content, lead gen forms, InMail, ABM ads, Thought Leader Ads, predictive audiences, B2B paid, or LinkedIn campaign.”.
- **02** When the source has headings, the agent prioritizes “Process / What to Analyze / Technical Setup (25% weight)” 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; mostly runs locally; usually needs no extra API key.

## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- 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: LinkedIn Ads deep analysis for B2B advertising. Evaluates 27 checks across technical setup, audience targeting, creative quality…
  • 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 “Process”, “What to Analyze”, “Technical Setup (25% weight)”, “Audience Targeting (25% weight)”, 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 ads-linkedin 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 “Process / What to Analyze / Technical Setup (25% weight)” 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 / 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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