Deep Audit
Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set...
Install
npx skills add pedrohcgs/claude-code-my-workflow --skill deep-audit -g -a claude-code -y- Repository
- pedrohcgs/claude-code-my-workflow1.7k
- Category
- Engineering
- Updated
- 1mo ago
- License
- MIT
- Context cost
- Mediumabout 2,712 tokens
- Runs scripts
- No, instructions only
What it does
Comprehensive adversarial audit of a theory, proof, math/econ paper, codebase, or set of claims, decompose into components, fan out independent skeptics that must return CONCRETE defects, adjudicate every finding with a separate judge, fix all confirmed defects, then re-verify. Use when correctness must be bulletproof and single-pass or round-by-round review is too slow and too shallow. Invoke for "audit this rigorously", "find ALL the bugs/gaps", "make this rock solid", "converge faster on correctness".
Deep adversarial audit
A convergent alternative to slow round-by-round review. Instead of one reviewer finding one or two issues per pass, fan out many independent skeptics over the whole artifact at once, adjudicate what they find, fix everything confirmed, and re-verify. Modeled on the multi-agent methodology behind hard formal-proof efforts (diverse independent portfolio, adversarial throughout, concrete evidence only, synthesize-challenge-repeat).
When to reach for this
- The artifact is dense enough that a single review keeps surfacing new issues each pass (the tell that round-by-round is the wrong tool).
- Correctness is the priority and the cost of a missed defect is high (a paper going to a top venue, a proof, a security-sensitive change, a migration).
- The user asked to "fix ALL of it", "be deeper", "converge faster", "100% rock solid".
Requires the user to have opted into multi-agent orchestration (they asked for a workflow / deep audit / to fan out agents, or ultracode is on). If they haven't, propose it and its rough cost first.
The method
1. Decompose (diverse portfolio). Break the artifact into components by idea, not by section: each independent claim, lemma, estimator, subsystem, invariant. Add cross-cutting failure-mode lenses (see below). Aim for coverage such that every load-bearing claim is attacked by at least one agent that is looking straight at it. Don't tell the agents your favored reading, preserve independence so they don't all converge on the same attractive-but-wrong conclusion.
