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.
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill best-option-selectiongit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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.
[](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/best-option-selection)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
- Invoke scoring-matrix-construction tactic to build the score matrix
- Select aggregation method based on problem characteristics (WSM for simple scenarios, TOPSIS when ideal solution reference is needed, VIKOR when compromise solution is needed)
- Invoke scoring-synthesis to produce final recommendation
- 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]
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.
- 9d ago First seen · 93 lines · 32 tokens per session scan A 8d05db9bb3b8
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.
Other skills, from other repositories
Deep Research
Produce a deep, structured research report on a topic: decompose into key dimensions, analyze each with evidence and reasoning, synthesize cross-cutting insights, and surface open questions. Use for deep research, analysis, and literature/landscape reviews.
literature-review-tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use to ..." for…
papers-reading-skill
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to…
paper-fulltext-harvest
Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall…
academic-figure-generation
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
paper-reading
Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden…