shadow-verify
SolidDispatch a parallel adversarial verifier wave after any high-stakes sub-agent investigation (code reviews, audits, findings reports, large refactors) — or whenever a sub-agent asserts a claim with high-confidence language (confident, certain, clearly, ≥80%), since confidence is a trigger, not a verdict. Shadow verifiers independently re-derive 2–3 key claims from scratch using tool calls only, returning CONFIRMED/REFUTED/UNVERIFIABLE, and flag disagreements before the user acts. Use when sub-agent output will drive decisions, file changes, commits, or external side-effects.
Install
Quality Score: 79/100
Skill Content
Details
- Author
- griffinwork40
- Repository
- griffinwork40/agent-afk
- Created
- 1 months ago
- Last Updated
- today
- Language
- TypeScript
- License
- Apache-2.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
shadow-attestation-verify
Verify a Shadow attestation record (Ed25519 or HMAC) without leaving Claude Desktop. Confirms request wasn't tampered, response wasn't tampered, the exact pinned model ran, and the deployment key material matches. Use when a bank auditor hands you a persisted JSON response and asks "is this real." Works cross-language — Python shadow-verify library produces byte-identical results for the same inputs on Python 3.9–3.13.
devils-advocate
Adversarially critique a proposal by generating alternatives. Dispatches 3 parallel critics (pragmatist, paranoid, architect lenses) — each invents one alternative approach — then a synthesis step ranks all 4 options and recommends the top choice. Use when a plan, fix, scoping, decomposition, or named recommendation will drive decisions and you want structured alternative-generation before committing. Complements /shadow-verify — that skill re-derives factual claims; this one critiques whether the chosen approach itself is best.
subagent-orchestration
Patterns for delegating work to parallel subagents with Claude Fable 5 — when to split tasks, async coordination, long-lived workers, and fresh-context verifiers. Use when designing multi-agent harnesses, when a large task has independent parts, when runs bottleneck on sequential steps, or when self-review keeps missing its own mistakes.