Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes.
复制安装指令,让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "math-olympiad" 技能: 1. 下载 https://raw.githubusercontent.com/anthropics/claude-plugins-official/main/plugins/math-olympiad/skills/math-olympiad/SKILL.md 2. 保存为 ~/.claude/skills/math-olympiad/SKILL.md 3. 装好后重载技能,告诉我可以用了
Tool policy: Solvers and verifiers use THINKING ONLY in the tight-budget workflow. Competition math is reasoning. Computation is for deep mode (§6c), and even then bounded — a recurrence that's doubly-exponential can't be computed past n~30, work mod 2^m instead.
| Problem | Approach | Verification |
|---|---|---|
| AIME numeric answer | Best-of-N → majority vote | Answer check only |
| Olympiad proof (IMO/Putnam/USAMO) | Full workflow below | 5-pass adversarial |
| "Is this proof correct?" | Skip to verification (step 4) | Adversarial + spec-gaming |
| Full problem set (e.g. all 6 from a competition) | Sequential: one full workflow per problem, collect results, compile single PDF | Per-problem adversarial |
Batch in one Workflow: Set opts.label on every agent() call to include
the problem ID (e.g., label: "P3:solver:2"). Without labels, 36 results come
back with no problem association. Run problems in parallel — the label is what
matters, not ordering.
Launch one solver workflow per problem (same VERBATIM prompt, different statement). Run them in parallel. When all return, run adversarial verification per problem. Problems that pass get their proof in the PDF; problems that abstain get "No confident solution" with partial notes.
Don't try to solve all N problems in one agent's context — each problem needs its own thinking budget and its own fresh-context verifier. The composition is mechanical: collect the per-problem outputs, fill in LaTeX sections, compile once. | "Simplify this proof" | Skip to presentation (step 8) | — |
Before solving anything, identify the interpretation.
Read the problem statement. List 2-3 ways it could be interpreted. For each: is this reading TRIVIAL? If one reading makes the problem easy and another makes it hard, the hard one is almost certainly intended. State which interpretation you're solving and WHY you believe it's the intended one.
The Aletheia case study found 50 of 63 "technically correct" solutions were for the wrong interpretation. Olympiad problems often have a trap easy reading.
Launch 8-12 attempt agents in parallel. Each agent internally iterates — solve → self-improve → self-verify → correct → repeat. This is the Yang-Huang structure that achieves 85.7% on IMO: one-shot solving isn't enough; per-attempt refinement matters.
The Agent tool cannot enforce tool restriction. Subagents get the full tool
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Manage Discord channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Discord channel.
Set up the Discord channel — save the bot token and review access policy. Use when the user pastes a Discord bot token, asks to configure Discord, asks "how do I set this up" or "who can reach me," or wants to check channel status.
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
Iterate on the Cardputer-Adv MicroPython app bundle (Claude Buddy, Snake, Hello) after the device is already provisioned via m5-onboard. Use when the user wants to add a new app, push a single changed .py without re-flashing, watch device serial logs, or run a one-shot REPL command. Trigger on "add an app", "push to the cardputer", "tail the device", "run on the device", or follow-up work after /maker-setup.
End-to-end onboarding for a freshly-plugged-in M5Stack ESP32 device (Cardputer, Cardputer-Adv, Core, CoreS3, Stick) — detect on USB, flash UIFlow 2.0 firmware, and install the Claude Buddy MicroPython app bundle. Use whenever the user plugs in or wants to flash/provision/reset an M5Stack or ESP32 board, or says "m5-onboard go".
Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts.