{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "zero-lab-ai-skills--engagement-plan-and-findings",
 "repo": "zero-lab-ai/skills",
 "repo_url": "https://github.com/zero-lab-ai/skills",
 "repo_slug": "zero-lab-ai-skills",
 "title": "Plan an authorized engagement and assemble findings",
 "page_title": "How to plan an authorized engagement and assemble findings with Skills",
 "summary": "Turn your rules of engagement into a plan, then build evidence-backed findings and a report draft.",
 "who": "A penetration tester or engagement lead starting a new authorized assessment who needs a structured plan and report trail.",
 "problem": "Engagement notes, evidence, and findings often drift apart, which weakens the final report.",
 "outcome": "A plan tied to your rules of engagement, plus findings that each point to their supporting evidence.",
 "steps": [
  "Install the plugin marketplace in Claude Code with `/plugin marketplace add zero-lab-ai/skills`.",
  "Run `/plugin menu` and select the engagement-operations bundle.",
  "Paste your signed rules of engagement and scope list into the session, and mark anything out of scope.",
  "Record each finding with the evidence item that supports it before writing the report section."
 ],
 "agent_prompt": "Using the engagement-operations skills from the zero-lab-ai/skills repo, draft an engagement plan for <client-name> based only on the rules of engagement I provide. Return the plan, a findings list with evidence references, and a report outline. Verify that no target or action appears that is outside the scope I gave.",
 "agents": [
  "claude-code",
  "codex"
 ],
 "difficulty": "Intermediate",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "The plan is only as good as the rules of engagement you supply, since the Skills do not expand scope on their own.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "python",
 "requirements": [
  "Written authorization and rules of engagement for the target system",
  "A coding agent that reads SKILL.md, such as Claude Code or Codex",
  "Supplied evidence for the assessment, such as tool definitions, exported policy, or test-tenant fixtures"
 ],
 "risk": "dual-use",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/zero-lab-ai-skills/engagement-plan-and-findings/",
 "md": "https://dedupedai.com/use-cases/zero-lab-ai-skills/engagement-plan-and-findings/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/zero-lab-ai-skills--engagement-plan-and-findings.json",
 "api_version": "v1"
}