Modeler Decision Logger

CommunityPopular

zhnnky329

modeler-decision-logger

Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.

Overview

Publisher	zhnnky329
Repository	`MathModeling-skills`
Skill name	`modeler-decision-logger`
Stars	1.1K
Forks	42
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Modeler Decision Logger AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/modeler-decision-logger
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/zhnnky329/MathModeling-skills/tree/main/.claude/skills/modeler-decision-logger

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/modeler-decision-logger .claude/skills/modeler-decision-logger

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Modeler Decision Logger 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 Modeler Decision Logger 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 Modeler Decision Logger 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

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

json
{
  "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.md only when the decision changes method state.
Update the manifest gate/status fields when present.

Decision Types

Typical values:

framing
method_choice
fallback_activation
result_verdict
stability_verdict
assumption_necessity
claim_scope
package_signoff
submission_authorization

Staleness

Mark a decision stale only when its cited evidence materially changed:

append a decision_stale record 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.

Legacy Migration

Read legacy:

methods/Qx/qx_decision_log.md
methods/Qx/decisions/*_modeler_decision.md

Migrate only completed human decisions. Preserve original timestamps and source paths when available. Do not convert PENDING placeholders into decisions.

Rules

Never choose, rationalize, strengthen, or complete the user's decision.
Never create a separate pending decision artifact.
Never copy ai_suggestion into the human rationale.
One evidence-linked sentence is enough; do not impose arbitrary prose length.
Preserve an honest AI-use provenance distinction.

Verification

JSONL is valid one-object-per-line.
Record is append-only and uniquely identified.
Human ownership and evidence are accurate.
Supersession and staleness preserve history.

Frequently asked questions

What does the Modeler Decision Logger AI skill do?

Why use Modeler Decision Logger on TypingMind?

Because you install it once and use it with any model. Modeler Decision Logger 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 Modeler Decision Logger in TypingMind?

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

Which AI models can use Modeler Decision Logger?

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 Modeler Decision Logger?

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

Is the Modeler Decision Logger 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.

View all

Docs Governance

affaan-m

CommunityPopular

Route broad documentation-governance requests to existing ECC skills and run an opt-in, read-only audit of mapped documentation roles, links, ADR indexes, and evidence references. Use when a user asks…

Ecc Conventions

Development conventions and patterns for ECC. JavaScript project with conventional commits.

Mcp Dependency Review

affaan-m

CommunityPopular

Statically review MCP configuration for mutable package references before approval or CI, without executing discovered MCP servers. Use when reviewing .mcp.json, Cursor, VS Code, Claude, Windsurf, or…

Skill Host Compat

Lint ECC skills for Codex/Cursor-safe frontmatter and Claude-only substitutions before a skill PR.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Purpose

Canonical Output

Required Fields

Workflow

Decision Types

Staleness

Legacy Migration

Rules

Verification

Frequently asked questions

Related AI Skills

Set up your own AI workspace now