Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nrdxp/predicate/ai-auditnpx skills add nrdxp/predicate --skill ai-auditgit clone --depth 1 https://github.com/nrdxp/predicateWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00054 | $0.01496 |
| Opus 5 | $0.00027 | $0.00748 |
| Sonnet 5 | $0.00011 | $0.00299 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
ai-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Generated Code Audit Workflow
Verification Dual — adversarial path. This skill is the applied methodology for the adversarial half of the Verification Dual: when no deterministic evaluator can be built for a condition in LLM-generated code, it is closed by decorrelated, context-free agents running this audit protocol from independent attractor basins. Load it whenever the symbolic path is unavailable and an adversarial review of AI-generated code is required.
A 4-layer framework for auditing LLM-generated code. Traditional SAST is insufficient—AI code is syntactically flawless but often logically "hollow."
Principle of Zero Trust: Treat every AI-generated line as a high-risk external contribution.
Layer 1: Inefficiency Taxonomy (ODC Framework)
LLMs prioritize token sequence probability over algorithmic optimization, creating systematic inefficiencies.
Research shows 0.74 correlation between General Logic failures and Readability/Maintainability issues.
Orthogonal Defect Classification (ODC)
| Category | Technical Trigger | The AI-ism | Remediation |
|---|---|---|---|
| Algorithm | Prime divisibility iterates to n instead of √n |
Inefficient iterative blocks; overly broad loop conditions | Narrow loop constraints; implement early stopping |
| Algorithm | O(n²) logic where O(n log n) is standard | Sub-optimal complexity; prioritizes "plausible" over optimal | Replace with standard library or optimized algorithms |
| Assignment | Used-before-assignment; shadowing built-ins (dict = {}) |
Shadowing & bloat; misuse of variable binding | Rename shadowed variables; ensure proper initialization |
| Interface | Accessing _internal members outside class scope |
Structural incoherence; poor class hierarchy integration | Enforce encapsulation; refactor to public APIs |
| Checking | Passes happy path but lacks try/except or null checks |
Partially wrong logic; failure to address edge cases (CWE-754) | Mandate input validation and exception traceability |
| Maintainability | Unnecessary else after return or break |
Defensive bloat; complex control flow without value | Flatten conditional logic; reduce cyclomatic complexity |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 147 lines · 54 tokens per session scan A 1ec7e2216ba2
ai-audit is a skill published in the GitHub repository nrdxp/predicate (10 stars, last pushed 9d ago), licensed MIT. It adds 54 tokens to every session and 1,496 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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