Agent skill

Workflow Orchestrator

by zhnnky329 in zhnnky329/MathModeling-skills

Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating…

MITAuto-check passed

Install Workflow Orchestrator

skills CLI
$ npx skills add zhnnky329/MathModeling-skills --skill workflow-orchestrator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zhnnky329/MathModeling-skills workflow-orchestrator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/workflow-orchestrator .claude/skills/workflow-orchestrator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
workflow-orchestrator
GitHub stars
1.1k
Token cost
~1.8k tokens
SKILL.md length
745 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating…

  • Works in 3 steps: planning/manifests/Qx.json → canonical artifacts on disk → legacy dashboard and legacy…
  • SKILL.md covers G1 — PROBLEM_FRAMED, G2 — METHOD_SCREENED, G2.5 — METHOD_CHOSEN_BY_HUMAN and G3 —…, plus 3 more sections
  • Calls git

What it does

Workflow Orchestrator is an agent skill from zhnnky329/MathModeling-skills. Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: 面向数学建模竞赛的 Claude Code / Codex Skills ,支持分阶段建模流程与 Python、MATLAB/北太天元代码分支。 The licence is MIT.

Example prompts

  • “/workflow-orchestrator”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. planning/manifests/Qx.json
  2. canonical artifacts on disk
  3. legacy dashboard and legacy method/decision artifacts

What it can do on your machine

Read from SKILL.md and the folder at commit 0b46e9c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Workflow Orchestrator loads about 1.8k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 745 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zhnnky329/MathModeling-skills at commit 0b46e9c, republished under its MIT licence (© zhnnky329). 745 words, ~1,751 tokens.

Download SKILL.mdSave it as .claude/skills/workflow-orchestrator/SKILL.md (or your agent's skills folder).
name
workflow-orchestrator
description
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.

Purpose

Act as the gate-driven scheduler and state reader. Do not solve models, write model code, or draft paper sections.

../../AGENTS.md is the packaged policy source. Prefer a project-root AGENTS.md when one exists; otherwise read the packaged copy relative to this SKILL.md. Apply that policy without reproducing large reports or dashboards.

Session Start

Before orchestration in a new workspace:

  • show git status --short;
  • check the chosen runtime and required core packages;
  • verify the workspace skeleton needed for the current request;
  • read planning/session_config.json, accepting legacy mode.

Report warnings concisely. Do not create the full project skeleton unless the user is initializing a project.

State Sources

Prefer, in order:

  1. planning/manifests/Qx.json
  2. canonical artifacts on disk
  3. legacy dashboard and legacy method/decision artifacts

Never trust a dashboard over newer canonical artifacts.

Manifest Contract

Maintain one compact JSON manifest per subquestion:

json
{
  "schema_version": 1,
  "question_id": "Q1",
  "rigor_profile": "lean",
  "current_gate": "G2",
  "status": "method_screened_waiting_human",
  "artifacts": {
    "method_card": "methods/Q1/q1_method_card.md",
    "decision_ledger": "methods/Q1/q1_decisions.jsonl",
    "risk_probe": "methods/Q1/probes/risk_probe_summary.json",
    "latest_run": null
  },
  "allowed": {
    "code_generation": false,
    "freeze": false,
    "paper_writing": false,
    "final_assembly": false
  },
  "blockers": [],
  "next_action": {
    "owner": "human",
    "skill": "decision-prompt-builder",
    "reason": "method choice not recorded"
  },
  "updated_at": "ISO-8601"
}

Update only fields affected by the current state change. Generate a human dashboard on request or at a milestone; otherwise derive status directly from manifests.

Gate Evaluation

Evaluate each Qx independently.

G1 — PROBLEM_FRAMED

Pass when parse, classification, data inventory, success criteria, and human framing exist. A placeholder in a human-owned field blocks the gate.

G2 — METHOD_SCREENED

Pass when:

  • qx_method_card.md defines a main candidate and usable baseline;
  • the baseline completes the real task with comparable output;
  • risk_probe_summary.json covers applicable checks, including output degeneracy;
  • main and baseline verdicts are PASS or justified CONDITIONAL;
  • any fallback has a concrete trigger.

Do not require a fixed number of candidates, universal PoCs, or a source-line limit.

G2.5 — METHOD_CHOSEN_BY_HUMAN

Pass when qx_decisions.jsonl contains a human DECIDED method choice citing probe evidence. While blocked, allow data preparation but not model code generation.

G3 — CODE_AND_EXPERIMENT_REVIEWED

Pass when:

  • approved main and baseline executed;
  • latest run_summary.json is complete;
  • language review contains passing named checks for syntax, input contract, method alignment, reproducibility, and output contract.

Accept legacy Markdown review artifacts during migration, but prefer JSON for new work.

G4 — RESULTS_JUDGED_AND_FROZEN

In lean, pass the result-judgment subgate when final-result and stability decisions cite computed evidence. Continue iterating without freezing when the human selects adjust or fallback.

In submission, additionally require:

  • final method explanation;
  • final result analysis;
  • robustness report;
  • package sign-off in the decision ledger;
  • solution package;
  • current frozen_numbers.json.

G5 — PAPER_SECTION_READY

Require the three writer rules, frozen-number sourcing, human-confirmed interpretation/claim scope, and verified figures.

G6 — FINAL_AUDIT_PASSED

Evaluate only in submission. Require passing consistency, completeness, and QA artifacts. Never infer that one auditor covers another.

Routing

Choose one primary next action:

  • missing framing → parser/classifier or human framing card;
  • missing data profile → data-auditor-cleaner;
  • missing method card/probe → method-selector;
  • missing human method choice → decision-prompt-builder;
  • approved method without implementation plan → model-code-analyzer;
  • code/review incomplete → language generator or reviewer;
  • meaningful experiment awaiting judgment → result choice card;
  • final results without robustness → robustness-checker;
  • submission package incomplete → final explainer, result report, or package builder;
  • paper ready but unaudited → the earliest missing final auditor.

Do not invoke several judgment-bearing skills speculatively.

Show full SKILL.md (270 more words)Show less

Change Impact

Classify changes before scheduling checks:

  • NONE: scratch, formatting, comments, non-semantic docs.
  • LOCAL: exploratory code or method-card updates before freeze.
  • CANONICAL: schema/units, symbols, equations, parameters, official values, figure paths.
  • FROZEN: changes affecting frozen values or paper claims.

Route checks:

  • NONE: none.
  • LOCAL: local tests/review.
  • CANONICAL: scoped consistency for affected Qx.
  • FROZEN: thaw log, rerun affected work, re-freeze, scoped consistency.

Never schedule a full-workspace consistency audit solely because more than one file changed.

Lean vs Submission

In lean:

  • require only manifests, method card, decision ledger, probe summary, and run summaries;
  • do not require per-round Markdown reports, full success logs, frozen numbers, paper artifacts, or final audits;
  • persist a detailed report only at a human decision point or final round.

In submission:

  • require final explanations, reviews, analyses, robustness, package, freeze, paper, and G6;
  • run the full three-auditor layer once before final assembly.

Compatibility

Read legacy artifacts when new ones are absent:

  • planning/progress_dashboard.md
  • qx_method_candidates.md
  • qx_method_iteration_log.md
  • qx_decision_log.md
  • decisions/*_modeler_decision.md
  • Markdown code reviews

Mark them legacy_source in the manifest and recommend migration at the next material edit. Do not regenerate legacy files for new work.

Output

Return a compact state report:

  • profile;
  • per-question current gate and blocker;
  • artifacts changed or missing;
  • change-impact class;
  • one next action;
  • optional runners-up only when they can proceed independently.

Do not paste a full dashboard or large JSON structure unless the user asks.

Verification

  • State is derived from current canonical artifacts.
  • Lean requirements are not confused with submission requirements.
  • Human decisions were not inferred from AI suggestions.
  • Code generation, freeze, paper writing, and final assembly flags match the gates.
  • Audit scope matches semantic impact.
  • Manifest and reported next action agree.

© zhnnky329, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .codex/skills/workflow-orchestrator of zhnnky329/MathModeling-skills.

Open the folder on GitHubat commit 0b46e9c

Compare with similar skills

Workflow Orchestrator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Workflow Orchestrator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Workflow Orchestrator this skillzhnnky329/MathModeling-skills1.1k—~1.8kAutomated safety check: PassMIT
Code Model Evaluation HarnessOrchestra-Research/AI-Research-SKILLs13k4 repos~2.9kAutomated safety check: PassMIT
Model Evaluationawslabs/agent-plugins916—~1.3kAutomated safety check: PassApache-2.0
Model Evaluation Metricsjeremylongshore/tons-of-skills-marketplace2.8k—~578Automated safety check: PassMIT
OmniRoute Model Catalogdiegosouzapw/OmniRoute75k—~589Automated safety check: PassMIT
Evaluating Machine Learning Modelsforyourhealth111-pixel/Vibe-Skills3.6k—~390Automated safety check: PassMIT

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Questions about Workflow Orchestrator

What does Workflow Orchestrator do?

Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating…. Workflow Orchestrator is an agent skill from zhnnky329/MathModeling-skills. Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream work.

How do I install Workflow Orchestrator in Claude Code?

Run `npx skills add zhnnky329/MathModeling-skills --skill workflow-orchestrator -a claude-code`. Or copy the skill folder (.codex/skills/workflow-orchestrator in zhnnky329/MathModeling-skills) into .claude/skills/workflow-orchestrator in your project. Claude Code loads it when a task matches its description.

How do I install Workflow Orchestrator in Codex?

Run `npx skills add zhnnky329/MathModeling-skills --skill workflow-orchestrator -a codex`. Or copy the skill folder (.codex/skills/workflow-orchestrator in zhnnky329/MathModeling-skills) into .agents/skills/workflow-orchestrator in your project. Codex loads it when a task matches its description.

Can I use Workflow Orchestrator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zhnnky329/MathModeling-skills --skill workflow-orchestrator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/workflow-orchestrator, .gemini/skills/workflow-orchestrator, .github/skills/workflow-orchestrator and .opencode/skills/workflow-orchestrator in your project.

What does Workflow Orchestrator need to run?

Going by SKILL.md and its folder, Workflow Orchestrator needs the command-line tools its instructions call (git).

Does Workflow Orchestrator access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Workflow Orchestrator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Workflow Orchestrator use?

Workflow Orchestrator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Workflow Orchestrator use?

About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Workflow Orchestrator?

Skills that share tags, products or a category with Workflow Orchestrator: Code Model Evaluation Harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Model Evaluation (awslabs/agent-plugins, 916 stars), Model Evaluation Metrics (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and OmniRoute Model Catalog (diegosouzapw/OmniRoute, 75k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Workflow Orchestrator?

zhnnky329 (a GitHub user) maintains it in zhnnky329/MathModeling-skills, which has 1,060 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 24, 2026.

Source: zhnnky329/MathModeling-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.