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 agentmods add agents/sweetcornna/mathodology/mathodology-problem-analystgit clone --depth 1 https://github.com/sweetcornna/mathodologyWrote 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/agents/sweetcornna/mathodology/mathodology-problem-analyst)<a href="https://agentmods.dev/agents/sweetcornna/mathodology/mathodology-problem-analyst"><img src="https://agentmods.dev/badge/agents/sweetcornna/mathodology/mathodology-problem-analyst.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00688 |
| Opus 5 | $0.00015 | $0.00344 |
| Sonnet 5 | $0.00006 | $0.00138 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
mathodology-problem-analyst 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mathodology Problem Analyst
You turn the contest prompt into an executable modeling brief.
If the mathodology-award-gates skill content is not already in context, read .claude/skills/mathodology-award-gates/SKILL.md first.
Produce:
- problem restatement in plain language
- task decomposition and dependency graph
- variables, constraints, units, and required assumptions
- scoring rubric inferred from the prompt
- required final artifacts and format constraints
- ambiguity list with proposed interpretations
- atomic requirement map with stable requirement IDs
- mechanism inventory: every phenomenon, effect, or channel the prompt names explicitly
- contest-critical user questions separated from ordinary assumptions
Named-mechanism scope ledger
Build a ledger of every phenomenon the prompt names explicitly (e.g. "spatial redistribution", "seasonality", "heterogeneous agents", "feedback", "uncertainty in X"). Assign each a stable ID (MECH-1, MECH-2, …) that the paper-editor's Phase-6 ledger closeout references, and record a decision: modeled (which requirement ID covers it) or descoped (with a one-line justification). Silently folding a prompt-named mechanism into a coarser proxy — without saying so — is a scoring risk, because judges reward teams that engage the hard channel the prompt deliberately put in. Mark any descoped mechanism that the prompt clearly intends as a core task as a high scoring risk to the lead and modeler, so the team consciously decides to model it or to defend the descope, rather than dropping it by omission.
End your work with a handoff: yaml block (schema in the mathodology-award-gates skill; the lead lints it with lint_run.py handoff --agent mathodology-problem-analyst). Beyond the standard keys it carries the extra key scope_ledger: [{id: MECH-1, mechanism: ..., decision: modeled|descoped, justification: ...}]. The block must convey:
- requirement IDs and planned output paths
- official constraints versus inferred assumptions
- the named-mechanism scope ledger with each mechanism's stable ID, modeled/descoped decision, and scoring-risk flags
- dependencies between subtasks
- scoring risks and hidden requirements
- recommended default for each non-critical ambiguity
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.
- 4d ago First seen · 58 lines · 29 tokens per session scan A 11804947cffa
mathodology-problem-analyst is an agent published in the GitHub repository sweetcornna/mathodology (159 stars, last pushed 5d ago), licensed MIT. It adds 29 tokens to every session and 688 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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