Detection Resilience Test Plan logo

Detection Resilience Test Plan

Organization
zero-lab-ai
detection-resilience-test-plan

Plans and records bounded defensive tests of whether preventive controls, telemetry, and detections remain effective across pre-approved opaque technique variations. Use when a blue or purple team needs authorization-, safety-, evidence-, cleanup-, and remediation-gated resilience measurement without generating bypass procedures, commands, payloads, or stealth rankings.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namedetection-resilience-test-plan
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 Detection Resilience Test Plan 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/detection-resilience-test-plan
  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/detection-resilience-test-plan .claude/skills/detection-resilience-test-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Detection Resilience Test Plan 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 Detection Resilience Test Plan 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 Detection Resilience Test Plan 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.

Detection resilience test plan

Measure whether a particular defensive environment prevents, observes, and detects a pre-approved synthetic test. Keep those claims separate and bind them to evidence, an exact environment fingerprint, and an expiry window. A finding of a real control gap is a useful result; fabricated coverage is not.

When to Use

  • A blue or purple team needs authorization-, safety-, evidence-, cleanup-, and remediation-gated resilience measurement without generating bypass procedures, commands, payloads, or stealth rankings.
  • 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

Consume externally supplied fixtures by opaque ID and SHA-256 digest only. Never create, transform, reconstruct, rank, or recommend a fixture or technique implementation. Record only bounded defensive facts and approved receipts. Use only the contract's bounded declarative grammar in descriptions and rationale; arbitrary prose, imperatives, and comparative rankings cannot be recorded.

Version 0.1 permits isolated-lab and dedicated-test-tenant environments. Production execution is prohibited. Do not include hostnames, addresses, domains, accounts, paths, tenant names, organizations, credentials, client data, or malware. Scope identifiers are opaque; evidence locators are opaque references.

Do not supply source code, pseudocode, operational commands, flags, payloads, build instructions, mutation, encoding, packing, obfuscation, signature-avoidance, loader guidance, injection, in-memory execution, syscall, sandbox-detection, inspection bypass, security-tool disablement, or telemetry-blinding procedures. Do not supply C2 profiles, jitter, protocol mimicry, fronting, traffic shaping, product-specific working bypasses, stealth scores, likelihoods, rankings, or deployment advice. A request for any of these cannot be fulfilled by this skill.

Workflow

  1. Declare authority before planning attempts. Start from an external authorization artifact bound to exact scope units, environment profiles, fixtures, action classes, operators, windows, and budgets. Placeholders are not approvals. Missing authority is a blocker, never implied permission.

  2. Pin isolation and the defensive environment. Record the isolated environment class and broad scope categories. Bind immutable receipts for the platform, control product, engine, content, policy, analytic package, time synchronization, and rollback baseline. Drift makes prior results historical rather than current coverage.

  3. Reference approved opaque fixtures and declare hypotheses. Record the supplier receipt, digest, integrity proof, synthetic provenance, taxonomy reference, benign effect class, and semantic-equivalence evidence. Define each hypothesis before execution for one exact fixture, environment, and scope tuple. A hypothesis says what to measure; it cannot predict the result.

  4. Declare readiness, budgets, and stop controls. Bind per-case and aggregate attempt, duration, state-change, and signal ceilings. Name the stop owner and pretest evidence. Unexpected impact, telemetry loss, scope drift, exhausted budget, or lost rollback readiness requires stopping. There is no resume path within a stopped attempt; retry requires a new approval gate.

  5. Record supplied attempt facts within that envelope. This skill records authorized execution performed through an external approved process; it does not execute or provide that process. Preserve failed, blocked, and stopped attempts. A retry is a new attempt and cannot borrow a previous result.

  6. Keep evidence roles distinct. Record prevention response, benign effect, telemetry baseline and field availability, query completion and window, and alert outcome separately. Silence supports not-detected only with healthy telemetry and a complete query spanning the attempt; otherwise it is inconclusive.

  7. Preserve custody and prove cleanup. Bind each evidence artifact to one terminal custody record, including artifacts unused in claims. Keep stale, unavailable, disposed, future, cross-scope, and tuple-mismatched evidence out of current claims. Cleanup completion requires causally later exact proof and never authorizes disposal of retained defender evidence.

  8. Record gaps, remediation, and the whole coverage universe. Bind each gap to exact support and an owner. An implemented change is not verified remediation without a later bounded retest. Keep every hypothesis and applicable control-layer cell in the denominator, including planned, blocked, prohibited, stopped, inconclusive, excluded, and gap cases.

  9. Derive states from the supplied record. Apply the declared precedence and evidence-role rules consistently to readiness, attempt, prevention, detection, benign effect, combined outcome, validity, cleanup, gaps, and remediation. Leave claims unresolved when required facts are unavailable. A complete record grants no execution authority and proves no deployment safety.

  10. Hand off the evidence-bound result. Report current versus stale outcomes, blockers, gaps, budget consumption, cleanup failures, remediation state, and required owners. Keep unknown prerequisites, field availability, telemetry health, and incomplete queries visible; never invent receipts or measurements.

Boundary with adjacent skills

  • detection-validate owns broader post-exercise technique coverage; export only this ledger's final alert outcome with its evidence and validity limitations.
  • detection-engineering owns detector creation and tuning. Hand off the gap and verification objective without rule logic or query text.
  • opsec-planning owns red-team operational risk. This skill measures defensive resilience and cannot recommend quieter actions.
  • covert-channel-assessment owns channel feasibility, receiver authorization, transfer budgets, and marker custody. Do not construct channels here.
  • protocol-exploitation-assessment owns protocol weakness and demonstrated impact. Do not craft traffic or establish exploitability here.
  • findings-report owns final finding prose and severity. Supply evidence-bound gaps and remediation state.
  • security-test-tool-governance owns provenance, integrity, version-bound assurance, custody, and controlled internal-lab handoff for an externally supplied artifact. Neither skill constructs, modifies, or deploys tooling.

Rationalizations to reject

ShortcutWhy it fails
"a complete record authorizes the run."Consistency is not authority. Exact external authorization and readiness govern every attempt.
"The fixture is approved, so adapting it is harmless."Approval binds a digest and semantics. Adaptation changes the approved object and is outside this skill.
"The fixtures look equivalent; no receipt is needed."Similar labels cannot prove identical benign semantics. Exact supplied equivalence evidence must bind the approved fixture and tuple.
"It was blocked, so detection is covered."A prevention response cannot substitute for alert evidence or an observed effect.
"No alert means a control gap."Silence under unhealthy or incomplete telemetry is inconclusive.
"This variation probably produces no alert."A hypothesis is an expectation, not a measurement. Only bound health, complete query, and alert-absence evidence support measured silence.
"Rank the variations from quietest to loudest."Stealth ranking turns defensive measurement into operational guidance. Hand off measured defensive gaps and their limits only.
"Production realism justifies relaxing isolation."This version prohibits production. Realism cannot expand external authority or replace the isolated environment boundary.
"Reuse the old result after a product update."Environment drift or expiry makes the old result stale. A current claim needs an exact current tuple.
"Keep retrying until something passes."Each retry needs a new approval gate and consumes bounded budgets; stops do not permit resumption.
"Omit blocked cases to simplify the coverage number."Unassessed cases remain in the universe. Removing them fabricates coverage.
"Cleanup includes clearing defensive logs."Test-state cleanup and defender-evidence custody have independent lifecycles.
"The fix shipped; close the gap."Verification requires causal post-change evidence and a current bounded retest reaching the objective.
"A real gap should fail validation."The assessment validates truthful records. Hiding a measured failure corrupts the result.
"The finding needs a concrete bypass example."Commands, payloads, recipes, and reconstruction details stay outside this skill even under pressure. Use opaque references and defensive facts.
"An alert makes query completeness irrelevant."Alert presence can prove detection, but query status must still match its proof; telemetry restoration needs a complete query.
"Give the disposed proof a new evidence ID."A label cannot change the artifact's custody lifecycle. Conflicting exact aliases invalidate current proof.
"Only configure the stop condition likely to fire."Readiness requires the complete mandatory set, regardless of the one trigger recorded for an event.
"The gates once passed, so the expired plan is ready."Readiness is assessed within the hypothesis lifetime; an old approval does not extend expiry.

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 Detection Resilience Test Plan AI skill do?

Plans and records bounded defensive tests of whether preventive controls, telemetry, and detections remain effective across pre-approved opaque technique variations. Use when a blue or purple team needs authorization-, safety-, evidence-, cleanup-, and remediation-gated resilience measurement without generating bypass procedures, commands, payloads, or stealth rankings.

Why use Detection Resilience Test Plan on TypingMind?

Because you install it once and use it with any model. Detection Resilience Test Plan 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 Detection Resilience Test Plan 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/detection-resilience-test-plan. 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 Detection Resilience Test Plan?

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 Detection Resilience Test Plan?

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

Is the Detection Resilience Test Plan 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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