Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ronmkr/PromptBook --skill math-olympiadgit clone --depth 1 https://github.com/ronmkr/PromptBookWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ronmkr/promptbook/math-olympiad)<a href="https://agentmods.dev/skills/ronmkr/promptbook/math-olympiad"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/math-olympiad/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ronmkr/promptbook/math-olympiad"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/math-olympiad.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00163 | $0.04533 |
| Opus 5 | $0.00081 | $0.02266 |
| Sonnet 5 | $0.00033 | $0.00907 |
| Haiku 4.5 | $0.00016 | $0.00453 |
Grade A, and why
math-olympiad scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to math-olympiad — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Math Olympiad Solver
The five things that change outcomes
- Strip thinking before verifying — a verifier that sees the reasoning is biased toward agreement. Fresh context, cleaned proof only.
- "Does this prove RH?" — if your theorem's specialization to ζ is a famous open problem, you have a gap. Most reliable red flag.
- Short proof → extract the general lemma — try 2×2 counterexamples. If general form is false, find what's special about THIS instance.
- Same gap twice → step back — the case split may be obscuring a unified argument. Three lines sometimes does what twelve pages couldn't.
- 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 |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/trigger_eval.json 2.0 KB
- references/adversarial_prompts.md 12 KB
- references/attempt_agent.md 1.9 KB
- references/known_constructions.md 2.0 KB
- references/model_tier_defaults.md 2.9 KB
- references/presentation_prompts.md 6.0 KB
- references/solver_heuristics.md 5.9 KB
- references/verifier_patterns.md 8.1 KB
- scripts/check_latex.sh 205 B runs code
- scripts/compile_pdf.sh 1.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 412 lines · 163 tokens per session scan A f697e195d343
math-olympiad is a skill published in the GitHub repository ronmkr/PromptBook (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 163 tokens to every session and 4,533 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to math-olympiad, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
browser-use
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, or extract information from web pages.
pi-autoresearch-loop
Skill "pi-autoresearch-loop" from twaldin/flt, covering pi-autoresearch — autonomous experiment loop, installation, quick start, core concepts and two-file persistence model.
grill
Rigorous requirements gathering through Socratic questioning. Use when the user says "/grill", "grill me", "ask me questions until we understand", or wants to be interrogated about implementation details before writing code. Forces deep thinking about edge cases, behavior, and design decisions before any code is…
grill-with-docs
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.