用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill adversarial-proof-audit命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | adversarial-proof-audit |
| description | Audit immutable mathematical artifacts through isolated evidence-focused review lenses |
| allowed-tools | ["Read","Write","Glob","Grep"] |
| metadata | {"specialization":"mathematics","domain":"science","category":"theorem-verification","phase":6} |
| graph | {"domains":["domain:mathematics"],"specializations":["specialization:computational-mathematics"],"skillAreas":["skill-area:mathematical-reasoning","skill-area:technical-writing"],"workflows":["workflow:research-validation","workflow:quality-convergence"],"roles":["role:research-scientist","role:computational-scientist"]} |
Obtain independent, location-specific challenge evidence rather than overlapping confidence judgments.
Immutable artifact path/SHA-256, validated registry, profile, round ID, rubric, gate manifest, and one assigned lens.
Do not read another lens report before submitting. Do not edit the artifact.
The four lenses are evidence roles, not four independent truth oracles. For a high-stakes external review, record for each reviewer the provider/model or human identity class, model/version when available, prompt-template version, tool access, and prior-report exposure. Use at least one reviewer from a different model family/provider or a qualified human for the reconstruction/counterexample lens when available. A same-family fallback is permitted only when its lack of diversity is disclosed; it must use a fresh context, must not see other reports, and must not be described as independent verification. Reviewer diversity reduces shared-failure risk but does not establish mathematical truth.
A proof assistant, SMT solver, CAS, exhaustive finite checker, or custom verifier counts only as scoped corroborating evidence. Record the exact claim checked, formal statement or encoding, assumptions/axioms, tool and version, command/configuration, input and output hashes, result, and the remaining informal translation gap. Tool success must never be generalized beyond the encoded claim or relabeled as verification of the complete proof.
Before an external publication or similarly consequential use, obtain an artifact-hash-bound review outside the authoring loop. The reviewer should reconstruct critical claims from definitions, inspect hypotheses and boundary cases, reproduce deterministic/formal-tool commands, sample-check formalization-to-prose correspondence, record disagreements, and sign/date a checklist with scope and unresolved items. Missing external verification is disclosed evidence debt, not silently treated as pass.
Stable finding ID; lens; obligation IDs; category; severity; exact path/locator; concrete input/state leading to failure; positive deduction; focused repair; evidence reference.
Write JSON, then run python validators/validate_grade.py ... with expectedExitCode: 0. Recompute score and deductions; reject stale hashes, duplicate IDs, unresolved obligations, perfect claims with findings/blockers/gate failures, or repair closure without a fresh round.
Material factual disagreement triggers a breakpoint. Majority vote is not resolution. reject-and-refine opens a focused obligation and forces a new artifact/hash, four fresh reports, and deterministic reruns. Stop after the configured bound.