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 chengziyue1222/math-model-agent --skill run-modeling-projectgit clone --depth 1 https://github.com/chengziyue1222/math-model-agentWrote 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/chengziyue1222/math-model-agent/run-modeling-project)<a href="https://agentmods.dev/skills/chengziyue1222/math-model-agent/run-modeling-project"><img src="https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/run-modeling-project/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/chengziyue1222/math-model-agent/run-modeling-project"><img src="https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/run-modeling-project.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 | $0.00048 | $0.01449 |
| Opus 5.5 | $0.00019 | $0.00580 |
| Sonnet 5.5 | $0.00010 | $0.00290 |
| Haiku 4.5 | $0.00005 | $0.00145 |
Grade A, and why
run-modeling-project 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 3d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Modeling Project
Plan each question's saved result, falsification check and figure purpose before solving. Keep the selected venue separate from the process profile; a failed check returns to the earliest affected stage and invalidates dependent figures and prose.
Coordinate the existing Skills around a strong final paper. The project record supports reproducibility, but it is not a second product or a substitute for modeling.
Workflow
- Read
references/workflow-profiles.mdand chooserapid,competition(the default), orauditbefore creating deliverables. - Read
references/distilled-competition-playbook.md, then record the problem, data sources, deliverables and venue constraints. Apply CUMCM-only layout rules only when that venue requires them. - Invoke
analyze-model-datawhere data is used andselect-modelfor decomposition, dependency analysis and structure opportunities. Classify each needed input as observed, externally supplied, model-derived, or unavailable. Before blocking on a missing derived input, decide whether the problem expects it to be estimated from information available at the decision time; if so, make that estimation a leakage-safe upstream subproblem. Invokeresearch-model-literaturewhen external facts, parameters, datasets, or citations affect a claim. - Invoke
solve-modelfor equations, algorithms, baselines, constraints, sensitivity and independent checks. - Invoke
make-model-figuresonly after identifying the conclusions each figure must establish. Use the sharedalgorithms.figuresregistry throughget_spec,list_figuresandget_figure. Select a dedicated renderer with fields matching the saved model results; usemode="formal". Inspect the live registry and generated capability matrix for current structured formal routes. Preview uses the same validators with explicitmode="preview", simulated=True. Calling successfully does not establish paper suitability; verify the actual output and its evidence. English keys and registered Chinese names identify the same renderer. Pass provenance, units, independent unit and actual interval bounds into the figure handoff; never synthesize missing statistics. Carry the user's visual preferences with the figure handoff. For a new end-to-end project or a cross-stage revision, readreferences/research-evidence-handoffs.md; reuse existing records rather than creating parallel ledgers. - Invoke
write-model-paperto turn verified results into a contest paper, thenreview-model-paperfor reader-visible and technical review. - In competition, produce a formal PDF by default (PDF or DOCX is sufficient) and a passing submission preflight. Provide a support archive and clean-directory reproduction when the selected venue requires them. In audit, add both document formats, independent review, HMAC and complete hash/producer evidence.
What ships with it
12 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.
- agents/openai.yaml 269 B
- references/distilled-competition-playbook.md 3.2 KB
- references/handoff-schema.md 1.2 KB
- references/paper-first-flow.md 628 B
- references/practical-evidence-flow.md 1.7 KB
- references/production-rule-pack.md 958 B
- references/research-evidence-handoffs.md 2.7 KB
- references/stage-gates.md 3.9 KB
- references/workflow-profiles.md 3.2 KB
- scripts/advance_project.py 3.5 KB runs code
- scripts/execute_skill.py 377 B runs code
- scripts/project_state.py 19 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.
- 3d ago Changed · +21 lines b94127b8c019
- 1mo ago Changed · +10 lines e3648856d0d3
- 2mo ago First seen · 40 lines · 48 tokens per session scan A 7aac4f43981e
run-modeling-project is a skill published in the GitHub repository chengziyue1222/math-model-agent (24 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 1,449 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.
run-modeling-project compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| run-modeling-projectchengziyue1222/math-model-agent | Skill | 24 | 4d ago | MIT | A | 48 | Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |