chain-of-verification

Chain-of-Verification (CoVe)

CoVe is a verification technique that improves response accuracy by making the model fact-check its own answers. Instead of accepting an initial response at face value, CoVe instructs the model to generate verification questions, answer them independently, and revise the original answer based on findings.

Conventions

• Read capy knowledge base conventions at shared-capy-knowledge-protocol.md.

Capy restriction: CoVe is a read-only verification tool. Do NOT call capy_index or capy_fetch_and_index during this workflow. Use capy_search only. If corrections reveal knowledge worth persisting, the calling agent handles indexing after CoVe completes.

When to Use This Skill

CoVe adds the most value in these scenarios:

Precision-required questions:

• Questions containing precision language ("exactly", "precisely", "specific")
• Complex factual questions (dates, statistics, specifications)

Complex reasoning:

• Multi-step reasoning chains (3+ logical dependencies)
• Technical claims about APIs, libraries, or version-specific behavior

Fact-checking scenarios:

• Historical facts, statistics, or quantitative data
• Technical specifications and API behavior

High-stakes accuracy:

• Security-critical code paths or analysis
• Code generation requiring accuracy verification
• Any response where correctness is critical

Self-correction triggers:

• When initial response contains hedging language ("I think", "probably", "might be")

Note: These heuristics can be copied to your project's CLAUDE.md if you want Claude to auto-invoke CoVe for matching scenarios. By default, CoVe requires manual invocation to give you control over when to invest additional tokens/time for verification.

Verification Modes

CoVe offers two verification modes to balance accuracy vs. cost:

Standard Mode (/kk:chain-of-verification)

Uses prompt-based isolation within a single conversation turn.