git:20260907.0cfcd93 to git:20261022.4d8df2f

- Mathodology is a skills pack for mathematical modeling contests. Use prompts to

- communication. Adapt the amount of work to the actual problem and deadline.

-

- ## Start from the task

-

- Read the problem and available data. Identify the decisions to support, the

- required outputs and the current contest rules. Ask only about missing details

- that would change the solution. State reasonable assumptions and keep working.

-

- Use a flexible cycle: understand the problem, formulate a model, challenge its

- results, and communicate the answer. Revisit any part when evidence changes.

- Do not require fixed phases, machine-readable handoffs, repeated approvals,

- arbitrary figure counts or invented award scores. Quality follows from the

- argument and evidence, not completion of a checklist or a predicted prize.

- - `mathodology-agent-pipeline`: adaptable modeling prompts and focused collaboration.

- Choose specialists only when their contribution helps the task and the host

- supports delegation. Brief them with a concrete question and relevant evidence;

- ask for findings, artifacts and limitations in ordinary prose. Independent

- review is useful for difficult mathematical or empirical claims. A single agent

- can complete a small task. Do not invent a mandatory panel or scoring system.

-

- ## Default figure guidance

-

- Use [the explicit agent guidance](.claude/skills/mathodology-figure-presets/references/figure-guidance.md)

- when starting or delegating figure work. The expected deliverable is a rendered,

- data-backed figure, with an explanation and reproduction path.

- ## Figures and image2

- Before designing figures, load `mathodology-figure-presets`. Default to its

- callable chart code templates for numerical figures. If image2 is unavailable

- or the answer is pending, bind real data, execute the template and deliver

- PNG/PDF outputs; do not stop at a design prompt or wait for image2. On the first figure

- request in each modeling task, ask once whether image2 is available through a

- current tool, a configured interface, or manual use. Reuse an answer already

- in the conversation; continue independent analysis while waiting. Follow the

- skill's image2 guidance, including truthful capability reporting and data-driven

- quantitative marks. Synthetic demonstrations must be labeled as such.

- Maintain skills in `.claude/skills/`, optional roles in `.claude/agents/`, and

- short workflow prompts in `.claude/workflows/`. This is the sole source of truth;

- `.agents/skills/` is a gitignored local installation mirror. Back up this project's

- mirror before refreshing it; do not modify other skills or global settings.

- ignored `work/` directory. Do not add application source, datasets, deployment,

- package manifests or build outputs. `.mcp.json` configures evidence search;

- keep credentials and machine-specific paths out of it. Historical application

- material remains available in Git history, outside the active skill set.