math-olympiad
Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses.
Skill by w0rp
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/w0rp-math-olympiad
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents
Reviews
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Shell scripts
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/math-olympiad
What's inside
- Skill name
math-olympiad- Description
- 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.
- License
- Unlicense
- Instructions
- 397 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (11)
- SKILL.md
- trigger_eval.json · evals/
- adversarial_prompts.md · references/
- attempt_agent.md · references/
- known_constructions.md · references/
- model_tier_defaults.md · references/
- presentation_prompts.md · references/
- solver_heuristics.md · references/
- verifier_patterns.md · references/
- check_latex.sh · scripts/
- compile_pdf.sh · scripts/
SKILL.md (first lines)
# Math Olympiad Solver ## The five things that change outcomes 1. **Strip thinking before verifying** — a verifier that sees the reasoning is biased toward agreement. Fresh context, cleaned proof only. 2. **"Does this prove RH?"** — if your theorem's specialization to ζ is a famous open problem, you have a gap. Most reliable red flag. 3. **Short proof → extract the general lemma** — try 2×2 counterexamples. If general form is false, find what's special about THIS instance. 4. **Same gap twice → step back** — the case split may be obscuring a unified argument. Three lines sometimes does what twelve pages couldn't. 5. **Say "no confident solution"** — wrong-and-confident is worse than honest abstain. --- **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. --- ## When to use which approach | 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. ### For a full problem set 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) | — | --- ## The Workflow ### 1. Interpretation check (30 seconds, catches 50/63 of one class of errors) 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. ### 2. Generate candidates with internal refinement (parallel, thinking only) 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 set. The only mechanism is the prompt. Use this prompt VERBATIM — do not summarize, do not synthesize your own: ``` NO COMPUTATION. Do not use Bash, Python, WebSearch, Read, Write, or any tool that runs code or fetches data. Numerical verification is not a proof step. "I computed n=1..10 and the pattern holds" is not a proof. (If your agent harness requires a StructuredOutput or similar return-mechanism tool call, that is NOT a computation tool — call it to return your answer. The restriction is on tools that DO work, not tools that REPORT work.) Your internal process (iterate until done): - Solve: Complete rigorous solution. - Self-improve: Reread. Fix gaps before a grader sees it. - Self-verify: Strict grader mode. Every step justified? - Correct: Fix and re-verify. Up to 5 rounds. - Stop: Self-verify passes twice clean, OR 5 rounds, OR approach fundamentally wrong. A correct answer from flawed reasoning is a failure. If incomplete, say so honestly. Never hide gaps. PROBLEM: <insert the problem statement here> ANGLE: <insert one starting angle here> ``` The first two paragraphs are load-bearing. A session that writes its own prompt and omits them will produce subagents that grind Python for 30 iterations and confidently get wrong answers — a pattern that fits n≤10 but fails at n=100 is not a proof. Starting angles (vary across agents — see `references/solver_heuristics.md`): - Work out small cases (test past n=3) - Look for an invariant or monovariant - Consider the extremal case - Try induction - What symmetries? - Work backwards - Drop a condition — where does it become trivially false? - Generalize (inventor's paradox — more structure is sometimes easier) Each returns its FINAL state (not intermediate rounds): ``` **Verdict**: complete solution | partial result | no progress **Rounds**: [how many verify→correct cycles] **Method**: [key idea, one paragraph] **Detailed Solution**: [full step-by-step, every step justified] **Answer**: [if applicable]
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Signals
- Stars
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- Last commit (this folder)
- Apr 22, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Unlicense
- Repository
- github.com/w0rp/claude-code-config
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
math-olympiad skill. Agent Mart. https://agentmart.dev/skills/w0rp-math-olympiad (last updated 2026-10-24)