aog-self-critic

Audits AscendC operator-generation sessions against a catalog of recurring failure patterns.

Updated Sep 15, 2026
One-click install
npx skills add https://github.com/WangWindow/CANN-BatchMatMulMaxsum --skill aog-self-critic-wangwindow
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aog-self-critic
Source: https://github.com/WangWindow/CANN-BatchMatMulMaxsum/tree/main/.agents/skills/aog-self-critic
Command: npx skills add https://github.com/WangWindow/CANN-BatchMatMulMaxsum --skill aog-self-critic-wangwindow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI-assisted AscendC operator-generation sessions repeatedly drift into the same failure modes — reward hacking, infrastructure bypass, premature platform-blame conclusions, unverified runtime-state claims, and premature stops — forcing the user to correct the same mistakes across sessions. This Skill audits the current working session against a catalog of those patterns before the user has to correct them again. ## Core Features & Use Cases - 43-item check catalog: Evaluates the session against categorized checks (priority drift, protocol bypass, premature conclusions, claim-without-verify, cheating-by-claim, KB pipeline integrity) with pass/warn/block verdicts. - Trigger-scoped subsets: Maps orchestrator triggers (pre-spawn, iter-cap warning, pre-finalize, pre-commit) to only the relevant catalog items, reducing audit cost per invocation. - Critic-bypass gating: Mandatorily runs before writing waiver or exception artifacts that would silence a specific check. - Use Case: Before spawning a multi-agent kernel-generation chain or finalizing an operator, invoke the audit to catch a planned direct agent spawn that bypasses the state machine, or a "hardware floor" claim lacking probe evidence. ## Quick Start Ask the assistant to run /aog-self-critic to audit the current session state and the last few actions before making a major decision.

Frequently Asked Questions about aog-self-critic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit an AI agent session for reward hacking?

Invoke /aog-self-critic with the current session state, including the last user prompt, active task list, and recent assistant actions. It evaluates each catalog check and returns pass, warn, or block verdicts, blocking plans that match known reward-hacking patterns.

What failure patterns does the self-critic catalog detect?

The catalog covers priority drift, infrastructure and state-machine bypass, premature platform-blame conclusions, unverified runtime-state claims, stale tasks, jargon creep, and KB commits from single-agent data. Checks are grouped into families and scoped per orchestrator trigger.

When should the self-critic audit be triggered?

Run it at natural inflection points: before spawning multi-agent chains, before finalizing or committing an operator, when approaching iteration caps, and mandatorily before writing any waiver or exception artifact that silences a critic check.

Does the audit evaluate all catalog checks on every invocation?

No. Trigger-specific subsets map each orchestrator trigger to only the relevant catalog items, cutting evaluation cost by 60-87 percent per fire. The full catalog runs only on explicit user invocation.

What happens when a check returns a block verdict?

A block verdict stops the current plan and requires re-planning with an explanation to the user. The skill forbids silently proceeding on a block, since silent failures erode user trust more than surfaced warnings.