ablation-planner
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated
Claude Code
$ npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer --agent claude-code
Codex
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a codex -y
Cursor
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a cursor -y
Gemini CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a gemini-cli -y
GitHub Copilot
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a github-copilot -y
OpenCode
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a opencode -y
Windsurf
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill proof-writer -a windsurf -y
name: proof-writer description: Writes rigorous mathematical proofs for ML/AI theory. Use when asked to prove a theorem, lemma, proposition, or corollary, fill in missing proof steps, formalize a proof sketch, 补全证明, 写证明, 证明某个命题, or determine whether a claimed proof can actually be completed under the stated assumptions. argument-hint: "[theorem-statement-and-assumptions]" allowed-tools: Read, Write, Edit, Grep, Glob, Skill(lean-formalize)
Write a mathematically honest proof package, not a polished fake proof.
Produce exactly one of: 1. a complete proof of the original claim 2. a corrected claim plus a proof of the corrected claim 3. a blockage report explaining why the claim is not currently justified
Extract and normalize:
If notation or assumptions are ambiguous, state the exact interpretation you are using before proving anything.
Determine the target proof file with this priority: 1. a file path explicitly specified by the user 2. a proof draft already referenced in local notes or theorem files 3. `PROOF_PACKAGE.md` in project root as the default target
Read the relevant local context:
Extract:
Restate:
Identify:
Preserve the user's original theorem statement unless a change is explicitly required. If you use a stronger normalization or cleaner internal formulation only to make the proof easier, keep that as an internal proof device rather than silently replacing the original claim.
When the user requests Lean, or formalizing a specific proof obligation would materially help establish correctness, invoke [`/lean-formalize`](../lean-formalize/SKILL.md) with the original statement, current attempt, and known dependencies. It develops the proof as well as its formal implementation. Return the checked scope, exported theorem and remaining obligations to this proof package. A partial result closes only its corresponding obligation. When called by `lean-formalize` to develop a missing mathematical argument, return that argument and remaining gaps to the calling Lean run; do not invoke `lean-formalize` recursively. Ordinary proof drafting does not require Lean; difficulty alone does not trigger it.
Before writing a proof, classify the claim into exactly one status:
Check explicitly:
If the claim is not provable as stated, do NOT fabricate a proof. Do NOT silently strengthen assumptions or narrow the theorem's scope just to make the proof work.
Choose a proof strategy, for example:
Then write a dependency map:
If one step is substantial, isolate it as a lemma instead of burying it in one sentence.
Write to the chosen target proof file.
If the target proof file already exists:
If the user does not specify a target, default to `PROOF_PACKAGE.md` in project root.
Do NOT write directly into paper sections or appendix `.tex` files unless the user explicitly asks for that target.
The proof package must include:
Mathematical rigor requirements:
Before finishing the target proof file, verify:
· · · · · · · · 💬 Join Community · 🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this…
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results",…
Search, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv",…
Autonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user…
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables.…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…