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verification-before-completion

Universal verification gate. MANDATORY before any completion claim, success assertion, commit, or PR. Evidence before claims, always.

SKILL.md

Full skill instructions

Verification Before Completion

Adapted from obra/​superpowers — integrated with agi verification scripts.

Overview

Claiming work is complete without verification is dishonesty, not efficiency.

Core principle: Evidence before claims, always.

Violating the letter of this rule is violating the spirit of this rule.


The Iron Law

NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE

If you haven't run the verification command in this message, you cannot claim it passes.


The Gate Function

BEFORE claiming any status or expressing satisfaction:

1. IDENTIFY: What command proves this claim?
2. RUN: Execute the FULL command (fresh, complete)
3. READ: Full output, check exit code, count failures
4. VERIFY: Does output confirm the claim?
   - If NO: State actual status with evidence
   - If YES: State claim WITH evidence
5. ONLY THEN: Make the claim

Skip any step = unverified, not verified

Evidence Requirements

ClaimRequiresNot Sufficient
Tests passTest command output: 0 failuresPrevious run, "should pass"
Linter cleanLinter output: 0 errorsPartial check, extrapolation
Build succeedsBuild command: exit 0Linter passing, logs look good
Bug fixedTest original symptom: passesCode changed, assumed fixed
Regression test worksRed-green cycle verifiedTest passes once
Agent completedVCS diff shows changesAgent reports "success"
Requirements metLine-by-line checklistTests passing

Integration with Agi Scripts

When available, use the project's verification scripts:

VerificationScriptCommand
Full auditchecklist.pypython .agent/​scripts/​checklist.py .
Security scansecurity_scan.pypython .agent/​skills/​vulnerability-scanner/​scripts/​security_scan.py
Lint checklint_runner.pypython .agent/​skills/​lint-and-validate/​scripts/​lint_runner.py
Teststest_runner.pypython .agent/​skills/​testing-patterns/​scripts/​test_runner.py
UX auditux_audit.pypython .agent/​skills/​frontend-design/​scripts/​ux_audit.py

If no project scripts exist, use the project's native test/​build commands.


Red Flags — STOP

If you catch yourself thinking:

  • Using "should", "probably", "seems to"
  • Expressing satisfaction before verification ("Great!", "Perfect!", "Done!")
  • About to commit/​push/​PR without verification
  • Trusting agent success reports without checking
  • Relying on partial verification
  • Thinking "just this once"
  • ANY wording implying success without having run verification

Rationalization Prevention

ExcuseReality
"Should work now"RUN the verification
"I'm confident"Confidence ≠ evidence
"Just this once"No exceptions
"Linter passed"Linter ≠ compiler ≠ tests
"Agent said success"Verify independently
"Partial check is enough"Partial proves nothing
"Different words so rule doesn't apply"Spirit over letter

Verification Patterns

Tests:

✅ [Run test command] [See: 34/​34 pass] "All tests pass"
❌ "Should pass now" / "Looks correct"

Build:

✅ [Run build] [See: exit 0] "Build passes"
❌ "Linter passed" (linter doesn't check compilation)

Requirements:

✅ Re-read plan → Create checklist → Verify each → Report gaps or completion
❌ "Tests pass, phase complete"

Agent delegation:

✅ Agent reports success → Check VCS diff → Verify changes → Report actual state
❌ Trust agent report

When to Apply

ALWAYS before:

  • ANY variation of success/​completion claims
  • ANY expression of satisfaction
  • Committing, PR creation, task completion
  • Moving to next task
  • Delegating to agents

The Bottom Line

No shortcuts for verification.

Run the command. Read the output. THEN claim the result.

This is non-negotiable.

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/​memory_manager.py auto --query "<task summary>"

Decision Tree:

  • Cache hit? Use cached response directly — no need to re-process.
  • Memory match? Inject context_chunks into your reasoning.
  • No match? Proceed normally, then store results:
python3 execution/​memory_manager.py store \
  --content "Description of what was decided/​solved" \
  --type decision \
  --tags verification-before-completion <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

  • Strategy: This skill communicates via the shared memory system.
  • Orchestration: Invoked by orchestrator via intelligent routing.
  • Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

  • Embeddings: nomic-embed-text via Qdrant memory system
  • Lightweight analysis: Local models reduce API costs for repetitive patterns