Security Test Tool Governance logo

Security Test Tool Governance

Organization
zero-lab-ai
security-test-tool-governance

Governs supplied benign security-test tool artifacts through purpose, authority, provenance, artifact integrity, safety-test evidence, version-bound review, custody, change control, and defensive handoff. Use when reviewing proposed security tooling or deciding whether an externally supplied artifact is ready for an authorized isolated test, without generating source code, payloads, build procedures, bypasses, or deployment instructions.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namesecurity-test-tool-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 Security Test Tool 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/engagement-operations/skills/security-test-tool-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/engagement-operations/skills/security-test-tool-governance .claude/skills/security-test-tool-governance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security Test Tool 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 Security Test Tool 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 Security Test Tool 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.

Security test tool governance

Produce an evidence-bound ledger for externally supplied opaque benign artifacts.

When to Use

  • Reviewing proposed security tooling or deciding whether an externally supplied artifact is ready for an authorized isolated test, without generating source code, payloads, build procedures, bypasses, or deployment instructions.
  • 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.

Boundary

Accept inventory facts and external receipts only. Never fetch, reconstruct, modify, compile, sign, execute, deploy, or distribute a referenced tool. Never provide source code, pseudocode, tool commands or flags, build recipes, payloads, loaders, injection or in-memory execution instructions, persistence, credential access, lateral movement, C2, covert channels, evasion, packing, obfuscation, signature avoidance, telemetry blinding, security-control tampering, target selection or locators, stealth rankings, or quieter-tool recommendations.

Only isolated labs and dedicated non-production test tenants with explicit separation evidence are supported. Release is an externally evidenced controlled handoff to an approved internal lab. Production use, customer deployment, public distribution, and operational use have no contract state. Restricted or unknown capabilities are inventory facts that force prohibition and quarantine; an authorization cannot waive that boundary.

Workflow

  1. Record purpose, policy snapshot, assessment cutoff, scope, authority window, permitted governance actions, operator roles, non-production boundary, and numeric ceilings. Missing authority remains visible; governance readiness never grants execution authority.

  2. Register each supplied artifact, source, configuration, supplier, component, and declared capability using only opaque references and exact SHA-256 digests. A digest establishes identity, not safety. Do not open opaque content to fill an evidence gap.

  3. Have the offline review recompute the versioned canonical manifest from candidate identity and sorted component/capability declarations. Any material change requires a replacement candidate and fresh downstream evidence; a supplied digest assertion cannot inherit prior approval.

  4. Pin the sandbox fingerprint, synthetic-data boundary, production separation, containment, rollback baseline, and validity window. Drift invalidates current claims even when the artifact bytes are unchanged.

  5. Instantiate all assurance criteria for every candidate before importing results. Applicability comes from the artifact-class matrix. Keep planned, failed, blocked, prohibited, inconclusive, and evidence-bound not-applicable cells in the denominator; absence is never a pass.

  6. Import separate provenance, integrity, dependency/source review, independent review, containment, functional, safety, observability, stop, and restoration receipts. Every supporting member must match its exact binding tuple and chronology. Supplier attestations cannot substitute for independent review; execution evidence cannot substitute for criterion-outcome evidence.

  7. Record current readiness gates, reserved and consumed budgets, applicable successfully pretested stop controls, custody readiness, and cleanup readiness. Derive readiness from these records, authority, identity, coverage, and issues. Preserve approval-required, blocked, and prohibited states without laundering them into lab-ready.

  8. Import facts from separately authorized bounded synthetic lab validation. This workflow does not perform it. Bind each run to its exact case, authority, candidate/manifest, sandbox/fingerprint, policy, gate, budget, stop, and cleanup records. Keep execution, criterion outcome, observability, stop event, and cleanup receipts distinct. Failed and stopped runs consume budget; a fired stop is terminal. A retry needs remaining capacity and a later decision.

  9. Preserve custody for each evidence artifact and its aliases. Keep failed, stopped, inconclusive, and unexecuted cases. Evidence disposal requires its own external authority and receipt; endpoint cleanup cannot authorize it. Record cleanup separately and retain blocked or failed restoration obligations.

  10. Derive issues and remediation from assurance failures and contradictions. A supplied change receipt does not close an issue. Record externally authorized change, a materially new replacement candidate, and later exact verification of the original criterion in an equivalent approved sandbox. Preserve the failed candidate, supporting evidence, and unresolved issues.

  11. Derive lab eligibility only with complete applicable passing assurance, current identities and approval, complete run cleanup, and no blocking or prohibited capability. Validate a supplied release occurrence receipt against the exact candidate, manifest, artifact, sandbox, scope, policy, authority, internal-lab recipient, channel, owner, and window. Have the review recompute the canonical handoff digest over the exact recipient and release facts; recipient class is only internal-lab, and channels are only controlled-repository, sealed-offline-media, or managed-lab-import; approval, occurrence, and withdrawal receipts must bind that digest. Changed handoff facts invalidate inherited receipts. Occurrence is not approval.

  12. Derive expiry and withdrawal obligations after authority expiry/revocation, policy or sandbox drift, material change, disposed evidence, prohibited behavior, or a blocking issue. A later exact withdrawal receipt records withdrawn. Export opaque blocker, cleanup, issue, remediation, and handoff IDs to defensive owners without operational instructions.

  13. Treat a complete review as an internally consistent record only. It never establishes authenticity, safety, execution authority, production suitability, or release permission.

Boundary with adjacent skills

detection-resilience-test-plan owns separate hypotheses, attempts, defensive outcomes, test cleanup, and detector remediation. This skill owns artifact provenance, assurance, custody, version validity, and defensive handoff beforehand. Neither constructs implementations. protocol-exploitation-assessment owns service weakness applicability and bounded reproduced faults; do not emit protocol requests or vulnerability proofs here. opsec-planning owns observer analysis; do not select quieter tools. recon-automation-governance owns jobs, targets, schedules, contact budgets, and orchestration; this skill owns candidate assurance. findings-report owns final findings and impact-derived severity. Export typed assurance issues here, without assigning vulnerability ratings. binary-fault-triage and binary-fault-validation-readiness own binary fault prerequisites; never derive exploit primitives or feasibility here. engagement-report-assembly owns the final engagement report; provide only the typed governance export.

Rationalizations to reject

  • “The lab is authorized, so generate or execute the helper.” Authority cannot waive the opaque-artifact boundary; import external facts instead.
  • “It is signed, reproducible, or hash-matched, so it is safe.” These establish limited identity/provenance facts and cannot replace independent assurance.
  • “The function is optional or disabled.” Restricted or unresolved inventory still forces prohibited readiness and quarantined lifecycle.
  • “The artifact barely changed; carry forward the review.” Material identity includes source, configuration, dependencies, capabilities, sandbox, and policy; mismatches invalidate current downstream claims.
  • “Discard failed cases or count only tested cells.” This hides the denominator and creates unsupported coverage. Preserve every required cell and result.
  • “A successful run proves every safety criterion.” Receipt roles and bindings are separate; a run cannot satisfy unrelated criteria.
  • “Cleanup permits deleting inconvenient evidence.” Custody disposition and endpoint restoration need independent authority and receipts.
  • “The fix or handoff happened, so approval is implied.” Changes require later verification; occurrence receipts never replace approval or withdrawal proof.

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 Security Test Tool Governance AI skill do?

Governs supplied benign security-test tool artifacts through purpose, authority, provenance, artifact integrity, safety-test evidence, version-bound review, custody, change control, and defensive handoff. Use when reviewing proposed security tooling or deciding whether an externally supplied artifact is ready for an authorized isolated test, without generating source code, payloads, build procedures, bypasses, or deployment instructions.

Why use Security Test Tool Governance on TypingMind?

Because you install it once and use it with any model. Security Test Tool 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 Security Test Tool Governance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/engagement-operations/skills/security-test-tool-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 Security Test Tool 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 Security Test Tool Governance?

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

Is the Security Test Tool 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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