Recon Automation Governance logo

Recon Automation Governance

Organization
zero-lab-ai
recon-automation-governance

Governs supplied reconnaissance job manifests with exact authority and scope binding, contact receipts, bounded waves and budgets, controlled retries, tested stop controls, custody, and defensive handoff. Use when reviewing or recording scheduled recon automation without generating commands, choosing targets, running reconnaissance, or expanding scope.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namerecon-automation-governance
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Recon Automation Governance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zero-lab-ai/skills/tree/main/plugins/recon-operations/skills/recon-automation-governance
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/recon-operations/skills/recon-automation-governance .claude/skills/recon-automation-governance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recon Automation Governance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Recon Automation Governance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Recon Automation Governance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Recon automation governance

Automation multiplies both consistency and mistakes. Govern supplied opaque job envelopes so a scheduler cannot turn a valid recon method into unauthorized scope, unbounded contact, silent retry, or persistent surveillance.

This skill is a control plane only. It does not acquire or select targets, classify a new action, generate commands or queries, choose tools, execute reconnaissance, evade detection, interpret results, build target profiles, or create findings.

When to Use

  • Reviewing or recording scheduled recon automation without generating commands, choosing targets, running reconnaissance, or expanding scope.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record current written authority, the immutable plan and configuration digests, exact opaque scope units, the engagement window, retention, and every prohibited output.
  2. Import passed upstream review receipts. osint-scanner admission requires its passive-plan receipt; network admission requires the exact network-recon scope receipt. A post-run scan coverage receipt cannot substitute for scope. Input production, checking, and validity must precede and contain the work they admit. Never reclassify or strengthen these decisions.
  3. Define each job as an opaque envelope with a fixed capability, posture, contact class, scope, input, authorization, wave, state, retry policy, and closed limitations. Store no target, endpoint, query, command, flag, identity, credential, or infrastructure detail. Unknown, unmapped, or posture-conflicting contact remains blocked.
  4. Bind every runnable job to one current authorization whose exact reverse sets cover its plan, configuration, inputs, scope, contact, budget, and execution window. Register each receipt once with one typed purpose and subject; a receipt or digest cannot prove two roles. Authority and its approval receipt must exist before every bound gate and cover each full attempt.
  5. Build an acyclic dependency graph. A prerequisite must be in an earlier wave, and a runnable job needs every dependency receipt satisfied.
  6. Require an initial approval gate for runnable work, a target-contact gate for target contact, and a distinct attempt-bound retry gate after each retryable failure. Its receipt and decision must exist after the prior outcome and before the next attempt.
  7. Reserve request, target-contact, runtime, rate, concurrency, storage, and attempt budgets before admission. Charge every attempt inside its budget window and reject per-job, wave, authorization, or global overflow.
  8. Keep retry reasons closed, attempt numbers contiguous, backoff nondecreasing, and retry counts bounded. Authority, scope, drift, custody, review, and operator-kill failures never retry.
  9. Quarantine discoveries as opaque IDs and source-job references only. Store no discovered target detail. A candidate may be rejected or handed to scope review, but its ID cannot enter a job, input, scope, or executable set without a new authority and immutable plan version.
  10. Arm and successfully test the owned global kill switch before any allow gate. A fired stop is terminal for this ledger; v0.1 has no resume state. Operator kill prevents later starts and retries across the complete job set.
  11. Bind attempt and stop evidence to digest-addressed artifacts, one custody record each, ordered retention and disposition, and opaque owners. Stale or disposed evidence proves no current completion and cannot enter a consuming handoff; retain only its opaque disposition receipt. No event, receipt, artifact, discovery, or attempt may postdate the analysis cutoff.
  12. Derive job, wave, and attempt states from gates, attempts, evidence, stops, and freshness. Never author complete, partial, failed, stale, blocked, or skipped by intuition.
  13. Create defensive handoffs only: upstream validation, scope review, coverage review, custody disposition, or specialist assessment. Governance output is not claim-bearing.
  14. Cover authority, scope, inputs, contact, dependencies, gates, budgets, retries, stop controls, custody, discovery quarantine, and handoff for every job with exact proof sets.

Boundary with adjacent skills

  • osint-scanner classifies passive contact; this skill trusts only its exact passed receipt.
  • network-recon scope receipts govern admission; its post-run coverage receipts support handoff only. Neither is interchangeable, and this skill imports no targets or commands.
  • opsec-planning owns objective risk, observability, abort, and cleanup reasoning.
  • target-profile-synthesis interprets validated evidence; governance output is boundary evidence.
  • findings-report owns demonstrated issues, severity, reproduction, and fixes.

Rationalizations to reject

  • "The upstream review passed, so the scheduler may run." One receipt proves one boundary.
  • "A discovered adjacent asset is implied scope." Quarantine it and request new authority.
  • "A retry is the same action." It consumes new quota, contact, time, and approval.
  • "Passive jobs are free." Third-party quotas, privacy, cost, retention, and policy still bind.
  • "Backoff makes every retry safe." Some stops prohibit another attempt entirely.
  • "One receipt can prove several controls." A digest proves only its typed subject and purpose.
  • "We can add the kill switch later." It must be armed and tested before runnable state.
  • "Failure left no evidence." Partial contact and errors still need custody and handoff.
  • "Stale means delete it now." Follow authorized disposition; keep only its opaque receipt.
  • "Unknown contact is probably harmless." Unknown and unmapped work remains blocked.
  • "Automation output is a finding." It is governance evidence until a specialist validates it.
  • "Unused budget can move anywhere." Allocation remains authority, scope, and class bound.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Recon Automation Governance AI skill do?

Governs supplied reconnaissance job manifests with exact authority and scope binding, contact receipts, bounded waves and budgets, controlled retries, tested stop controls, custody, and defensive handoff. Use when reviewing or recording scheduled recon automation without generating commands, choosing targets, running reconnaissance, or expanding scope.

Why use Recon Automation Governance on TypingMind?

Because you install it once and use it with any model. Recon Automation Governance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Recon Automation Governance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/recon-operations/skills/recon-automation-governance. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Recon Automation Governance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Recon Automation Governance?

As many as you like. As long as a model supports skills, you can use Recon Automation Governance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Recon Automation Governance AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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