best-option-selection

best-option-selection is a skill for Claude Code, Codex from yogsoth-ai/de-anthropocentric-research-engine. It costs 32 tokens per session (605 once invoked), scanned A, original, Apache-2.0.

A decision-making skill that selects one option from several candidates using formal scoring methods such as weighted scoring, TOPSIS, AHP, MAUT, or VIKOR. These methods compare options across multiple criteria and trade-offs.

In plain words
What is it for?
Use it to define decision criteria, assign weights, score candidates, compare normalized results, and produce one recommendation.
Why use it?
It replaces an informal choice with a repeatable comparison that makes criteria, weights, and scores visible.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to define decision criteria, assign weights, score candidates, compare normalized results, and produce one recommendation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill best-option-selection
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for best-option-selection

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection/github.svg)](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for best-option-selection

Your own site · 80×15
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 605 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.00605
Opus 5 $0.00016 $0.00302
Sonnet 5 $0.00006 $0.00121
Haiku 4.5 $0.00003 $0.00060

Measured 9d ago against content hash 8d05db9bb3b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

best-option-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/best-option-selection/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Best-Option Selection

Purpose: Select the single best-performing alternative from a candidate set, supporting WSM, TOPSIS, AHP, MAUT, VIKOR, and other methods.

When to use:

  • User needs to select "the best one" from multiple candidates
  • Decision scenario allows compensatory trade-offs (high scores offset low scores)
  • Moderate number of candidates (3-15)

Budget

Base SOP Target ±10% Range
criterion-definition 5-8 criteria 4-9
weight-elicitation-sop 1 weight vector 1
alternative-scoring 1 score matrix 1
normalization 1 normalized matrix 1
scoring-synthesis 1 recommendation 1

State Ledger

strategy: best-option-selection
status: pending
criteria_defined: false
weights_computed: false
scores_computed: false
normalized: false
synthesized: false
selected_method: null
result: null

Available Tactics

  • scoring-matrix-construction — Standard workflow: define criteria → assign weights → score → aggregate → sensitivity

Available SOPs

Import (from scoring-matrix-construction)

  • criterion-definition
  • weight-elicitation-sop
  • alternative-scoring
  • normalization

Subagent

  • scoring-synthesis

Execution Guidance

  1. Invoke scoring-matrix-construction tactic to build the score matrix
  2. Select aggregation method based on problem characteristics (WSM for simple scenarios, TOPSIS when ideal solution reference is needed, VIKOR when compromise solution is needed)
  3. Invoke scoring-synthesis to produce final recommendation
  4. If user questions the result, switch methods, recompute, and compare

Output Format

## Best Option Recommendation

**Recommended:** [Alternative name]
**Overall Score:** [Score value]
**Method Used:** [WSM/TOPSIS/AHP/MAUT/VIKOR]

### Score Ranking
| Rank | Alternative | Overall Score | Key Strengths |
|------|-------------|---------------|---------------|

### Sensitivity Notes
[Impact of weight changes on the result]

Read the full file on GitHub · 93 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 93 lines · 32 tokens per session scan A 8d05db9bb3b8

Subscribe to this mod's changes

best-option-selection is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 3d ago), licensed Apache-2.0. It adds 32 tokens to every session and 605 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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