fluxly Search skills… ⌘K 中文 Publish

ads-audit

Security Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Security
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-audit.preview 
---
name: ads-audit
description: Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ad…
category: security
runtime: no special runtime
---

# ads-audit output preview

## PART A: Task fit
- Use case: Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, Microsoft Ads, and Apple Ads accounts via 6 parallel audit agents. Amazon Ads, cross-platform attribution, and server-side tracking are covered by their standalone sub-skills (ads-amazon, ads-attribution, ads-server-side-tracking) — Wave 3 will add their paired agents so they can dispatch in parallel here. Generates health score per platform and aggregate score (0-100). Use when user says audit, full ad check, analyze my ads, account health check, paid media audit, paid advertising audit, ad spend audit, advertising audit, or PPC audit..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Process / Data Collection / Scoring” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, Microsoft Ads, and Apple Ads accounts via 6 parallel audit agents. Amazon Ads, cross-platform attribution, and server-side tracking are covered by their standalone sub-skills (ads-amazon, ads-attribution, ads-server-side-tracking) — Wave 3 will add their paired agents so they can dispatch in parallel here. Generates health score per platform and aggregate score (0-100). Use when user says audit, full ad check, analyze my ads, account health check, paid media audit, paid advertising audit, ad spend audit, advertising audit, or PPC audit.”.
- **02** When the source has headings, the agent prioritizes “Process / Data Collection / Scoring” 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: Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTo…
  • 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”, “Data Collection”, “Scoring”, “Per-Platform Weights”, 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-audit 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 / Data Collection / Scoring” 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

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