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Workflow Orchestrator

CommunityPopular
zhnnky329
workflow-orchestrator

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.

Overview

Publisherzhnnky329
RepositoryMathModeling-skills
Skill nameworkflow-orchestrator
Stars
1.1K
Forks
42
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zhnnky329 on GitHub. Read the source before you install it.

Installation

Install the Workflow Orchestrator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/workflow-orchestrator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills.git /tmp/MathModeling-skills
mkdir -p .claude/skills
cp -r /tmp/MathModeling-skills/.claude/skills/workflow-orchestrator .claude/skills/workflow-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow Orchestrator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Workflow Orchestrator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Workflow Orchestrator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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.

Frequently asked questions

What does the Workflow Orchestrator AI skill 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 downstream work.

Why use Workflow Orchestrator on TypingMind?

Because you install it once and use it with any model. Workflow Orchestrator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Workflow Orchestrator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/workflow-orchestrator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Workflow Orchestrator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Workflow Orchestrator?

As many as you like. As long as a model supports skills, you can use Workflow Orchestrator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Workflow Orchestrator AI skill free?

Yes. It is published on GitHub by zhnnky329 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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