Defensive Control Evidence Synthesis logo

Defensive Control Evidence Synthesis

Organization
zero-lab-ai
defensive-control-evidence-synthesis

Synthesizes supplied evidence-backed defensive assessment results and administrative receipts into a scope-bound control-evidence matrix that preserves lineage, freshness, contradictions, unknowns, and gaps. Use when consolidating defensive control evidence without scanning, scoring, certifying, or rewriting findings.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namedefensive-control-evidence-synthesis
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 Defensive Control Evidence Synthesis 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/detection-validation/skills/defensive-control-evidence-synthesis
  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/detection-validation/skills/defensive-control-evidence-synthesis .claude/skills/defensive-control-evidence-synthesis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Defensive Control Evidence Synthesis 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 Defensive Control Evidence Synthesis 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 Defensive Control Evidence Synthesis 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.

Defensive control evidence synthesis

When to Use

  • Consolidating defensive control evidence without scanning, scoring, certifying, or rewriting findings.
  • 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.

Purpose

Build and validate a closed matrix from already-supplied specialist artifacts and sanitized administrative, validation, or remediation-verification receipts.

Boundary

Never contact a system, scan, sample, query, test, or execute. Reject raw logs, configurations, ACLs, credentials, tickets, certificates, packets, live identifiers, commands, queries, rules, test bodies, attack paths, and remediation procedures. Never create, rewrite, merge, close, or severity-rank findings. Never calculate a posture score, grade, maturity level, compliance result, certification, or exploitability rank.

Workflow

  1. Accept only exact passed specialist artifacts and sanitized administrative, validation, or remediation-verification receipts already supplied.
  2. Stop on operational or restricted content. Do not quote rejected values.
  3. Preserve producer, artifact format, artifact digest, review receipt, scope, policy, time, window, origin lineage, upstream class, polarity, limitations, applicability, and strength.
  4. Report only generic diagnostic codes; never reflect restricted values.
  5. Keep all ten policy dimensions in the exact scope-by-dimension denominator.
  6. Do not strengthen, reverse, or reinterpret an upstream claim. Preserve or weaken it only when freshness or custody requires that result.
  7. Cap administrative evidence at documented or configured.
  8. Treat a review pass as artifact-structure evidence, not proof of no exposure or proof that a control works.
  9. Keep telemetry, detection, containment, and recovery evidence independent.
  10. Accept a no-finding claim only from exact, complete, current upstream coverage.
  11. Deduplicate shared origin lineage before assessing independence.
  12. Require one open contradiction for eligible opposing independent claims.
  13. Use not-applicable only when source, claim, binding, and cell carry the same exact current policy reason.
  14. Require one immutable finding reference for each current or stale, otherwise-valid exposure claim; do not edit the upstream finding.
  15. Treat remediation occurrence as unverified until a later exact verification receipt independently binds the earlier occurrence, scope, and dimension.
  16. Keep evidence gaps administrative. They are not technical findings.
  17. Keep handoffs exact-bound, chronological, defensive, and non-authorizing.
  18. Return exact state counts, contradiction IDs, gap IDs, and finding-reference IDs. Never produce a score.

Report

State the matrix identifier, exact state counts, contradictions, evidence gaps, and immutable finding references. Add: “Internal synthesis consistency only; no live interaction, score, compliance determination, certification, or finding modification occurred.”

Rationalizations to reject

  • “The record is complete, so there is no exposure.” Completeness validates the supplied artifact contract, not the absence of findings.
  • “Configured means validated.” Administrative receipts cannot prove observation or validation.
  • “Telemetry means detection.” Collection evidence does not prove a detection outcome.
  • “Detection means containment or recovery.” These are separate claims.
  • “Two copies are independent.” Shared origin lineage is one source.
  • “Implementation closed the gap.” Occurrence is not later verification.
  • “A score makes this easier.” Aggregation would erase unknowns and disputes.
  • “We can clean up upstream findings.” This skill never rewrites them.

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 Defensive Control Evidence Synthesis AI skill do?

Synthesizes supplied evidence-backed defensive assessment results and administrative receipts into a scope-bound control-evidence matrix that preserves lineage, freshness, contradictions, unknowns, and gaps. Use when consolidating defensive control evidence without scanning, scoring, certifying, or rewriting findings.

Why use Defensive Control Evidence Synthesis on TypingMind?

Because you install it once and use it with any model. Defensive Control Evidence Synthesis 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 Defensive Control Evidence Synthesis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/detection-validation/skills/defensive-control-evidence-synthesis. 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 Defensive Control Evidence Synthesis?

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 Defensive Control Evidence Synthesis?

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

Is the Defensive Control Evidence Synthesis 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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