MCPInstall

cavekit-validation-first

Validation-first design for Cavekit — every kit requirement must be automatically verifiable. Six-gate validation pipeline, phase gates, merge protocol, completion signals, acceptance criteria patterns. Use when designing acceptance criteria, wiring CI gates, or debugging why an agent is producing output that nobody can verify.

Installs
106
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/juliusbrussee/caveman-code --skill cavekit-validation-first
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads cavekit-validation-first automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Validation-First Design

Core Principle: If an Agent Cannot Validate It, It Will Not Be Met

Every spec requirement must include testable acceptance criteria that an agent can automatically verify. This is not optional — it is the foundation that makes SDD work.

Why? AI agents are non-deterministic. Without automated validation, there is no way to know whether an agent's output is correct. Validation gates turn "the agent generated some code" into "the agent generated code that provably meets the specification."

The validation-first rule applies at every level:

  • Spec requirements must have testable acceptance criteria
  • Plans must define which gates verify each task
  • Implementation must pass all applicable gates before being considered complete
  • Iterations must show measurable progress through gates

The Validation Gate Sequence

Every implementation must pass through six ordered checkpoints. Each successive gate is more expensive to run, so catching failures early saves significant time.

Gate 1: Compilation Check

What: The project compiles/transpiles without errors.

# Generic pattern — substitute your project's build command
{BUILD_COMMAND}

Why it matters: If the code does not build, nothing else can be validated. This is the cheapest possible check.

What it catches:

  • Syntax errors
  • Missing imports/dependencies
  • Type errors (in typed languages)
  • Configuration errors

Acceptance criteria pattern:

- [ ] `{BUILD_COMMAND}` completes with exit code 0
- [ ] No warnings related to {domain} (warnings in other domains are acceptable)

Gate 2: Isolated Unit Verification

What: Unit tests pass on all changed files.

# Generic pattern
{TEST_COMMAND}

# Or targeted at changed files
{TEST_COMMAND} --filter {changed-files}

Why it matters: Unit tests verify individual functions and modules in isolation. They are fast, deterministic, and catch logic errors.

What it catches:

  • Incorrect function behavior
  • Edge cases not handled
  • Regression from changes to existing code
  • Contract violations (wrong return types, missing fields)

Acceptance criteria pattern:

- [ ] All existing unit tests pass
- [ ] New unit tests cover all acceptance criteria for R{N}
- [ ] No test relies on external services or network access

Gate 3: Cross-Component Integration

What: End-to-end and integration tests verify that components work together.

# Generic pattern
{TEST_COMMAND} --e2e

# Or with a specific test runner
{E2E_TEST_COMMAND}

Why it matters: Unit tests verify components in isolation. Integration tests verify they work together. Many bugs only appear at integration boundaries.

What it catches:

  • API contract mismatches between components
  • Data flow errors across module boundaries
  • Authentication/authorization integration issues
  • Database query errors with real (or realistic) data

Acceptance criteria pattern:

- [ ] User can complete {workflow} end-to-end
- [ ] API endpoint returns correct response for {scenario}
- [ ] Error propagation works correctly from {source} to {destination}

Gate 4: Resource and Speed Benchmarks

What: Performance benchmarks pass defined thresholds.

# Generic pattern
{BENCHMARK_COMMAND}

# Or specific checks
{TEST_COMMAND} --performance

Why it matters: Functional correctness is necessary but not sufficient. Performance regression can make a feature unusable even if it produces correct output.

What it catches:

  • Response time regression
  • Memory leaks or excessive allocation
  • CPU-intensive operations that block the main thread
  • Database query performance degradation

Acceptance criteria pattern:

- [ ] API response time < {N}ms at p95 under {M} concurrent users
- [ ] Page load time < {N}s on simulated 3G connection
- [ ] Memory usage does not exceed {N}MB during {operation}
- [ ] No operation blocks the main thread for > {N}ms

Note: Not every task needs performance gates. Apply Gate 4 when:

  • The spec explicitly defines performance requirements
  • The change touches a known hot path
  • The feature involves data processing at scale

Gate 5: Startup Smoke Test

What: The application starts successfully and basic smoke tests pass.

# Generic pattern — start the application
{START_COMMAND}

# Verify it is running
curl -f http://localhost:{PORT}/health

# Or run smoke tests
{SMOKE_TEST_COMMAND}

Why it matters: Code can build and pass all tests but fail to start. Launch verification catches configuration issues, missing environment variables, port conflicts, and startup race conditions.

What it catches:

  • Missing environment variables or configuration
  • Port conflicts or binding errors
  • Startup initialization failures
  • Missing runtime dependencies
  • Database migration issues

Acceptance criteria pattern:

- [ ] Application starts with `{START_COMMAND}` and responds to health check
- [ ] Main screen/page renders without errors
- [ ] No error-level entries in application logs during startup
- [ ] Application shuts down gracefully on interrupt signal

Gate 6: Manual Audit

What: A human reviews the output for quality, design intent, and requirements that are difficult to automate.

Why it matters: Some things cannot be automated — UX quality, architectural elegance, naming consistency, documentation clarity. Gate 6 is where the human acts as the final quality filter.

What it catches:

  • Subjective quality issues
  • Architectural decisions that are technically correct but strategically wrong
  • Over-engineering or under-engineering
  • Security concerns that automated tools miss
  • Requirements that were technically met but miss the spirit of the spec

How it works in practice:

  1. Agent completes Gates 1-5 and reports results
  2. Human reviews the implementation against spec intent
  3. Human provides feedback as issues or spec updates
  4. If feedback requires code changes, it enters the revision loop:
    • Update specs with the missing requirement
    • Re-run iteration loop
    • Verify the fix emerges from updated specs

Acceptance criteria pattern:

- [ ] Implementation reviewed by human for spec intent alignment
- [ ] No architectural concerns raised
- [ ] Code style consistent with project conventions

Gate Summary Table

GatePurposeCommand PatternTypical DurationAutomated?
1. CompilationCode compiles cleanly{BUILD_COMMAND}SecondsYes
2. Unit VerificationIndividual functions behave correctly{TEST_COMMAND}Seconds-MinutesYes
3. IntegrationModules cooperate as expected{E2E_TEST_COMMAND}MinutesYes
4. BenchmarksSpeed and resource use within budget{BENCHMARK_COMMAND}MinutesYes
5. Smoke TestApplication boots and responds{START_COMMAND} + health checkSecondsYes
6. Manual AuditMeets design intent and quality barHuman inspectionVariableNo

For the full validation gate reference with detailed examples, see references/validation-gates.md.


Mapping Spec Requirements to Gates

Every spec requirement must map to at least one validation gate. When writing specs (see ck:cavekit-writing), each acceptance criterion should indicate which gate verifies it.

Mapping Pattern

### R1: User Authentication
**Acceptance Criteria:**
- [ ] Valid credentials return session token — **Gate 2** (unit test)
- [ ] Invalid credentials return 401 error — **Gate 2** (unit test)
- [ ] Session token grants access to protected endpoints — **Gate 3** (integration)
- [ ] Login page renders within 2s — **Gate 4** (performance)
- [ ] Application starts with auth module loaded — **Gate 5** (launch)

Unmapped Requirements Are Unvalidated

If a requirement cannot be mapped to any gate, it has one of two problems:

  1. The requirement is too vague — rewrite it with specific, testable criteria
  2. The validation infrastructure is missing — add the gate (e.g., if there are no E2E tests, Gate 3 does not exist yet)

Either way, an unmapped requirement will not be reliably met by an agent.


Phase Gates Between Hunt Phases

Phase gates are mandatory verification checkpoints between Hunt phases. They ensure that the output of one phase is solid before the next phase builds on it.

Phase Gate Definitions

TransitionGate ConditionHow to Verify
Spec → PlanAll domains have specs with testable acceptance criteriaReview cavekit-overview.md; every R{N} has AC items
Plan → ImplementPlans reference specs, define sequence, include test strategiesReview plan files; every task maps to spec requirements
Implement → IterateCode builds (Gate 1), tests pass (Gate 2), impl tracking is currentRun {BUILD_COMMAND} and {TEST_COMMAND}; check impl tracking
Iterate → MonitorConvergence detected: changes decreasing iteration-over-iterationCompare diffs across last 3-5 iterations
Monitor → SpecGap found or new requirement identifiedGap analysis identifies unmet acceptance criteria

Phase Gate Enforcement

Phase gates are enforced by the iteration loop. When a prompt includes phase gate checks, the agent:

  1. Runs the gate check at the end of the phase
  2. Reports pass/fail status
  3. If the gate fails, the agent does not proceed to the next phase
  4. Instead, the agent iterates on the current phase until the gate passes

Example: Implement → Iterate Gate

## Exit Criteria (Phase Gate)
Before reporting completion:
- [ ] `{BUILD_COMMAND}` succeeds with exit code 0
- [ ] `{TEST_COMMAND}` passes with no new failures
- [ ] All files created/modified are listed in impl tracking
- [ ] All dead ends encountered are documented
- [ ] Test health table is updated with current counts

Merge Protocol

When working with agent teams (multiple agents dispatched with isolation: "worktree" via the Agent tool), the merge protocol ensures that integrating work from different agents does not break validation gates.

The Protocol

Agent A completes work in its isolated branch
Agent B completes work in its isolated branch
Agent C completes work in its isolated branch

Merge sequence (one at a time):
1. Merge Agent A's branch → main
2. Run: {BUILD_COMMAND} → must pass
3. Run: {TEST_COMMAND} → must pass
4. Run: Launch verification → must pass
5. If all pass → proceed
6. If any fail → fix before merging next branch

7. Merge Agent B's branch → main
8. Run: {BUILD_COMMAND} → must pass
9. Run: {TEST_COMMAND} → must pass
10. ...repeat for each agent branch

Why One at a Time?

Merging all agent branches simultaneously and then running tests makes it impossible to determine which merge caused a failure. Merging one at a time with validation between each merge pinpoints failures immediately.

Merge Protocol Rules

  1. Merge one agent branch at a time — never batch-merge
  2. Run Gates 1-3 after each merge — build, unit tests, integration tests
  3. Run Gate 5 after all merges — launch verification on the fully integrated codebase
  4. Clean up after each merge: remove the worktree (git worktree remove <path>), then delete the branch (git branch -D <branch>). Claude Code only auto-cleans worktrees when agents make no changes; when changes are committed, the caller must remove the worktree before deleting the branch.
  5. If a merge fails validation, fix it before proceeding to the next merge

Completion Signals

Completion signals are specific strings that agents emit when all exit criteria for a task or phase are met. They enable automation to detect when an agent is done.

How Completion Signals Work

  1. The prompt defines the signal:

    When ALL exit criteria are met, output exactly:
    <all-tasks-complete>
    
  2. The agent emits the signal after verifying all exit criteria

  3. The iteration loop detects the signal and stops iterating

Completion Signal Rules

  • The signal must be a unique string that would not appear in normal output
  • The agent must verify all exit criteria before emitting the signal
  • The signal should be the last thing the agent outputs in a session
  • If the agent cannot meet all criteria, it should not emit the signal and instead document what is blocking completion

Example Prompt with Completion Signal

## Exit Criteria
Complete all of the following before emitting the completion signal:
- [ ] All T- tasks are DONE or BLOCKED with documented blockers
- [ ] `{BUILD_COMMAND}` succeeds
- [ ] `{TEST_COMMAND}` passes with no new failures
- [ ] Implementation tracking is updated
- [ ] All dead ends are documented

When ALL criteria above are met, output:
<all-tasks-complete>

If you cannot meet all criteria, document what is blocking
and do NOT output the completion signal.

Validation-First Design Patterns

Pattern 1: Test Before Implement

Write or generate tests before implementing the feature. The test defines what "correct" means.

1. Read spec requirement R{N} acceptance criteria
2. Generate test cases that verify each criterion
3. Run tests → all fail (RED)
4. Implement the feature
5. Run tests → all pass (GREEN)
6. Refactor if needed

This is TDD-within-SDD. See superpowers:test-driven-development for the existing TDD skill.

Pattern 2: Gate Cascade

Run gates in order. If an earlier gate fails, do not run later gates.

Gate 1 (Build) → FAIL → fix build errors → retry Gate 1
Gate 1 (Build) → PASS → Gate 2 (Unit Tests)
Gate 2 (Tests) → FAIL → fix failing tests → retry Gate 2
Gate 2 (Tests) → PASS → Gate 3 (Integration)
...

Earlier gates are cheaper. Fixing a build error costs seconds. Fixing an integration error costs minutes. Fix cheap problems first.

Pattern 3: Progressive Gate Depth

Not every iteration needs all gates. Use progressive depth based on the phase:

PhaseRequired GatesOptional Gates
Early Implement1 (Build), 2 (Unit)—
Mid Implement1, 2, 3 (Integration)4 (Performance)
Late Implement1, 2, 3, 5 (Launch)4
Pre-ReleaseAll 1-6—

Pattern 4: Regression Prevention

When a gate that previously passed starts failing, treat it as a P0 issue:

  1. Stop forward progress — do not implement new features
  2. Identify the regression — which change caused the failure?
  3. Fix the regression — restore the passing state
  4. Add a test — ensure this specific regression cannot recur
  5. Backpropagate — if the regression reveals a spec gap, update the spec

Integration with Other Skills

With superpowers:verification-before-completion

The existing verification-before-completion skill provides a general framework for verifying work before marking it done. Validation-first design extends this with the specific 6-gate pipeline and phase gate system used in SDD.

How they work together:

  • superpowers:verification-before-completion ensures the agent checks its work
  • ck:validation-first defines exactly what checks to run and in what order

With ck:cavekit-writing

Every spec requirement must have acceptance criteria that map to validation gates. The spec-writing skill defines how to write those criteria. Validation-first design defines how to verify them.

With ck:impl-tracking

Validation results are recorded in the implementation tracking document's Test Health table. Gate failures become Issues. Gate-related dead ends are documented in the Dead Ends section.

With ck:methodology

Validation gates operate continuously across all Hunt phases. Phase gates control transitions between phases. The iteration loop uses gate results as convergence signals.


Summary

  1. If an agent cannot validate it, it will not be met — every requirement needs automated verification
  2. 6 gates in order: Compilation → Unit Verification → Integration → Benchmarks → Smoke Test → Manual Audit
  3. Earlier gates are cheaper — catch problems at the build stage, not at launch
  4. Every spec requirement maps to at least one gate — unmapped requirements are unvalidated
  5. Phase gates control Hunt transitions — do not proceed until the current phase passes its gate
  6. Merge one at a time — validate between each merge to pinpoint failures
  7. Completion signals enable automation — agents emit a specific string when all gates pass
  8. Regression is P0 — when a passing gate starts failing, stop and fix before proceeding

FAQ

What does the cavekit-validation-first skill do?

Validation-first design for Cavekit — every kit requirement must be automatically verifiable. Six-gate validation pipeline, phase gates, merge protocol, completion signals, acceptance criteria patterns. Use when designing acceptance criteria, wiring CI gates, or debugging why an agent is producing output that nobody can verify.

How do I install cavekit-validation-first?

Run npx skills add https://github.com/juliusbrussee/caveman-code --skill cavekit-validation-first in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cavekit-validation-first/ (personal) or .claude/skills/cavekit-validation-first/ (project).

Which AI agents can use cavekit-validation-first?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is cavekit-validation-first safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains cavekit-validation-first?

It is published by juliusbrussee in juliusbrussee/caveman-code. It has 106 recorded installs.

More from juliusbrussee/caveman-code

View all →

caveman-compress

juliusbrussee/caveman-code

Ultra-compressed communication mode (lite / full / ultra) that cuts token usage 75% by speaking like caveman while keeping full technical accuracy. Use when the user requests "caveman mode", "less tokens", "be brief", or when output budget is tight.

136 installs

cavekit-revision

juliusbrussee/caveman-code

Trace bugs and manual fixes back to kits and prompts; fix at the source so the iteration loop can reproduce the fix autonomously. Six-step revision process plus the single-failure backpropagation protocol. Use when a manual hot-fix has been applied, when convergence stalls, or when the same class of bug keeps reappearing.

AI & Agents107 installs

cavekit-design-system

juliusbrussee/caveman-code

Open the skill to read its SKILL.md instructions.

Frontend & Design106 installs

plugin-creator

juliusbrussee/caveman-code

Scaffold a complete cave plugin bundle — generates .cave-plugin/plugin.json manifest and the standard directory structure (commands/, skills/, agents/, themes/, hooks/). Use when a user wants to create, publish, or package a cave plugin for the marketplace. Triggered by "create a plugin", "scaffold a plugin", "/plugin create", or "new cave plugin".

106 installs

cavekit-methodology

juliusbrussee/caveman-code

Open the skill to read its SKILL.md instructions.

105 installs

dynamic-resources

juliusbrussee/caveman-code

Example skill loaded from resourcesdiscover

100 installs

Popular devops & cloud skills

View category →

microsoft-foundry

microsoft/azure-skills

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure

OfficialDevOps & Cloud579.1K installs

azure-diagnostics

microsoft/azure-skills

Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.

OfficialDevOps & Cloud569.8K installs

azure-prepare

microsoft/azure-skills

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

OfficialDevOps & Cloud569K installs

azure-ai

microsoft/azure-skills

Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.

OfficialDevOps & Cloud568.8K installs

azure-deploy

microsoft/azure-skills

Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.

OfficialDevOps & Cloud568.6K installs

azure-validate

microsoft/azure-skills

Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

OfficialDevOps & Cloud568K installs