Aggregate Ranking

Organization

yogsoth-ai

aggregate-ranking

Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`aggregate-ranking`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
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 yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Aggregate Ranking 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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/aggregate-ranking
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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/aggregate-ranking

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/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/aggregate-ranking .claude/skills/aggregate-ranking

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

Use it in TypingMind

Enable Aggregate Ranking 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 Aggregate Ranking 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 Aggregate Ranking 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.

aggregate-ranking

Purpose

Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.

Input contract

yaml
required: [candidate_set, criterion_results, aggregation_rule]
optional: [tie_break_rule, missing_value_policy, uncertainty_annotations]
constraints: [criterion directions and scales must be declared; no silent imputation]

Procedure

Align candidate identifiers, criterion directions, units, and validity flags.
Apply the supplied aggregation rule without changing weights or directions.
Propagate missingness and uncertainty; apply the declared tie-break only after aggregation.
Return ordered candidates with component contributions and recommendation status.

Output contract

yaml
produces: [ordered_recommendation, aggregate_scores, contribution_table, unresolved_comparisons]
delta_fields: [findings, evidence_updates, decisions, uncertainties]

Quality gates

Every ranked candidate has a traceable value for each required criterion or an explicit unresolved marker.
Aggregation reproduces the supplied rule and preserves criterion direction.
Ties and sensitivity to tie-breaks are reported.

Parameterization

Caller supplies candidate schema, criterion scales/directions, weights or aggregation formula, tie-break rule, and missing/uncertainty policy.

Failure and counterexamples

Reject mixed units without normalization; reject a recommendation when a hard criterion is unresolved.

Provenance map

resolved: priority-synthesis
resolved: scoring-synthesis

Preserved source criteria ledger

source	criterion
priority-synthesis	All scoring dimensions are present for every gap.
priority-synthesis	Weight vector sums to 1.0 within +/-0.001.
priority-synthesis	Priority list is sorted descending; ties use feasibility sub-score.
priority-synthesis	Top N is N=min(3,total gaps) and includes attack-path suggestions.
scoring-synthesis	Final recommendation includes recommended alternative, confidence, key assumptions, and risk warnings.

Frequently asked questions

What does the Aggregate Ranking AI skill do?

Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.

Why use Aggregate Ranking on TypingMind?

Because you install it once and use it with any model. Aggregate Ranking 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 Aggregate Ranking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/aggregate-ranking. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aggregate Ranking?

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 Aggregate Ranking?

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

Is the Aggregate Ranking AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 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

aggregate-ranking

Purpose

Input contract

Procedure

Output contract

Quality gates

Parameterization

Failure and counterexamples

Provenance map

Preserved source criteria ledger

Frequently asked questions

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