MathModeling-skills is a collection of Claude Code and Codex skills for mathematical modeling competitions, guiding users through data analysis, method-specific checks, reproducible computations, figures, and paper audits without taking over modeling decisions. It is for contestants who want AI to handle mechanical checks while they retain responsibility for choosing methods and interpreting results. The catalogue entries form the project's skills, instructions, hook, setting, and plugin workflow.
Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/modeler-decision-logger/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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/zhnnky329/mathmodeling-skills/modeler-decision-logger)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/modeler-decision-logger"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/modeler-decision-logger/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/zhnnky329/mathmodeling-skills/modeler-decision-logger"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/modeler-decision-logger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.00704 |
| Opus 5.5 | $0.00020 | $0.00282 |
| Sonnet 5.5 | $0.00010 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
modeler-decision-logger 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 2mo 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Make human judgment traceable without multiplying decision files.
Canonical Output
methods/Qx/qx_decisions.jsonl
One JSON object per line. Records are append-only.
Use planning/framing_decisions.jsonl for global/pre-Qx framing decisions.
Required Fields
{
"schema_version": 1,
"decision_id": "q1_method_choice",
"decision_type": "method_choice",
"status": "DECIDED",
"decided_by": "human",
"captured_in_mode": "learning",
"choice": "M2",
"rationale": "Human-authored reason tied to evidence.",
"evidence_refs": ["methods/Q1/probes/risk_probe_summary.json"],
"decided_at": "ISO-8601",
"supersedes": null
}
Optional structured fields may include confidence, rejected alternatives, round action, claim scope, assumption labels, or fallback activation.
Workflow
- Receive the human's answer, the choice-card ID, and evidence paths.
- Preserve the user's meaning and wording. Normalize only structure, identifiers, and whitespace.
- Verify:
- the choice is one of the presented options or explicitly records a user-supplied alternative;
- evidence paths exist;
- rationale is non-empty and contains no placeholder;
- the record does not falsely label AI-authored prose as human-authored.
- Append one JSON line.
- If revising a decision, append a new record with
supersedes; never overwrite history. - Update the compact history in
qx_method_card.mdonly when the decision changes method state. - Update the manifest gate/status fields when present.
Decision Types
Typical values:
framingmethod_choicefallback_activationresult_verdictstability_verdictassumption_necessityclaim_scopepackage_signoffsubmission_authorization
Staleness
Mark a decision stale only when its cited evidence materially changed:
- append a
decision_stalerecord naming the old decision and changed evidence; - ask the human to reconfirm through one choice card;
- do not mark decisions stale because unrelated files or formatting changed.
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.
- 2mo ago First seen · 97 lines · 49 tokens per session scan A 70470d8a304e
modeler-decision-logger is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 704 once invoked, about $0.0002 per session on Opus 5.5. 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.
modeler-decision-logger compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| modeler-decision-loggerzhnnky329/MathModeling-skills | Skill | 1.1k | 1mo ago | MIT | A | 49 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |