Result Report Generator

CommunityPopular

zhnnky329

result-report-generator

Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.

Overview

Publisher	zhnnky329
Repository	`MathModeling-skills`
Skill name	`result-report-generator`
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 Result Report Generator 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/result-report-generator
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/result-report-generator

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/result-report-generator .claude/skills/result-report-generator

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

Use it in TypingMind

Enable Result Report Generator 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 Result Report Generator 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 Result Report Generator 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

Turn saved experiment artifacts into compact evidence. Do not treat ordinary successful runs as requiring a long report, and do not choose the winning method.

Inputs

run_summary.json
method card and probe summary
decision ledger
saved tables, metrics, and figures
session rigor_profile

Stop if the run summary claims outputs that do not exist or if main and baseline are not comparable.

Modes

Ordinary lean round

Validate the run summary and referenced artifacts.
Return a compact evidence digest in the conversation.
Do not save a Markdown report unless:
- a fallback trigger fired;
- a material anomaly or contradiction exists;
- the human must make a proceed/adjust/fallback decision.

Decision-point round

Save:

results/Qx/experiments/roundN/qx_decision_report.md

Include only:

main vs baseline metrics;
output-degeneracy/concentration evidence;
assumption or feasibility warnings;
robustness evidence already available;
fallback trigger state;
unresolved trade-offs.

Then invoke decision-prompt-builder. After the human answers, route the answer to modeler-decision-logger.

Final/submission mode

Save:

results/Qx/reports/qx_final_result_analysis.md

Include:

final main/baseline comparison;
uncertainty and error;
concentration/degeneracy interpretation;
robustness links;
limitations and applicable scope;
exact source paths for numerical claims.

Rejection and Fallback

Archive a method only after a human result_verdict or fallback_activation decision.
Move rejected code and outputs to workspace/archived/<Qx>/<method>_REJECTED_roundN/.
Add one compact history line to qx_method_card.md; do not create a separate iteration log.
Do not archive from an AI suggestion alone.

Rules

Do not fabricate metrics, comparisons, or interpretations.
Separate facts from human verdicts.
Do not create result-report-generator_modeler_decision.md.
Do not repeat the full run summary; cite it and extract only decision-relevant evidence.
Do not call a diagnostic reference a usable baseline.
Do not generate paper prose.

Verification

Every reported number resolves to a saved artifact.
Main/baseline comparison uses the same split, unit, and metric definition.
Output concentration and fallback trigger are addressed.
Reports are generated only at decision points or final mode.
Human verdicts are read from or appended to the canonical JSONL ledger.

Frequently asked questions

What does the Result Report Generator AI skill do?

Why use Result Report Generator on TypingMind?

Because you install it once and use it with any model. Result Report Generator 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 Result Report Generator in TypingMind?

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

Which AI models can use Result Report Generator?

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 Result Report Generator?

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

Is the Result Report Generator 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Purpose

Inputs

Modes

Ordinary lean round

Decision-point round

Final/submission mode

Rejection and Fallback

Rules

Verification

Frequently asked questions

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