ablation-planner
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
Rigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via cross-model review (external reviewer backend, ultra reasoning), fixes each gap with full derivations, re-reviews, and generates an audit report. Use when user says "检查证明",
Claude Code
$ npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker --agent claude-code
Codex
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a codex -y
Cursor
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a cursor -y
Gemini CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a gemini-cli -y
GitHub Copilot
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a github-copilot -y
OpenCode
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a opencode -y
Windsurf
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-checker -a windsurf -y
name: proof-checker description: Rigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via cross-model review (external reviewer backend, ultra reasoning), fixes each gap with full derivations, re-reviews, and generates an audit report. Use when user says "检查证明", "verify proof", "proof check", "审证明", "check this proof", or wants rigorous mathematical verification of a theory paper. argument-hint: "[path-to-tex-file or proof-description] [--deep-fix] [--restatement-check]" allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, Agent(aris-fanout-leaf), mcp__codex__codex, mcp__codex__codex-reply, mcp__manual_review__review, mcp__manual_review__review_reply, Skill(lean-formalize), mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status
> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md): > resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP) > with `reviewer_route.py` through the canonical helper resolver, preserving this > slot's tier, fresh contexts, source/image inputs and deterministic gates. > Codex examples/pins below apply only to `backend=codex`; other routes use their > own first-call/reply contract. Save the route and actual returned identity; > unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual > families/completion/scope. Forward invocation reviewer/model/effort overrides > to reviewer-bearing children. Generation workers are unchanged.
> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It is > verdict-bearing — it judges proof validity across rounds, threading the > reviewer's memory from Phase 1 → Phase 3 via `codex-reply` so the reviewer can > check whether a fix actually closed the gap it flagged. An external timer > re-enters from the top each tick, starting a fresh thread and losing that > memory. Schedule the *external wait that precedes it*, not the verdict. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
Systematically verify a mathematical proof via cross-model adversarial review, fix identified gaps, re-review until convergence, and generate a detailed audit report with proof-obligation accounting.
When calling the reviewer, branch on REVIEWER_BACKEND:
**If REVIEWER_BACKEND = `codex`:** Use `mcp__codex__codex` for new review threads (`model: gpt-6-astra`, `config: {"model_reasoning_effort": "ultra"}`). Use `mcp__codex__codex-reply` for follow-up rounds (reuse threadId).
**If REVIEWER_BACKEND = `manual`:** Use `mcp__manual_review__review` for new review threads with: prompt: [exact same prompt that would go to Codex] config: {"model_reasoning_effort": "xhigh", "executor_model": "<actual executor model>", "require_reviewer_model": true} Save the returned `threadId`. Use `mcp__manual_review__review_reply` for follow-up rounds with: threadId: [saved manual-review threadId] prompt: [follow-up prompt] config: {"model_reasoning_effort": "xhigh", "executor_model": "<actual executor model>", "require_reviewer_model": true}
Prompt fidelity: the manual prompt must be exactly the same text that Codex would receive. Review tracing applies equally to both backends.
The proof passes when ALL of the following hold: 1. Zero open FATAL or CRITICAL issues 2. Every theorem/lemma has: (i) explicit hypotheses, (ii) proof with all interchanges justified, (iii) every application discharges hypotheses in the ledger 3. All big-O/Θ/o statements have declared parameter dependence and uniformity scope 4. Counterexample pass executed on all key lemmas (log candidates even if none found)
| Category | Description | Example | |----------|-------------|---------| | **UNJUSTIFIED_ASS
· · · · · · · · 💬 Join Community · 🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.
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