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 category-sortinggit 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/category-sorting)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/category-sorting"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/category-sorting/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/category-sorting"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/category-sorting.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 | $0.00028 | $0.00640 |
| Opus 5 | $0.00014 | $0.00320 |
| Sonnet 5 | $0.00006 | $0.00128 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
category-sorting 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Category Sorting
Purpose: Classify candidate alternatives into predefined categories (e.g., A/B/C grades, compliant/non-compliant), supporting ELECTRE-Tri, FlowSort, AHPSort, DRSA, and other classification methods.
When to use:
- User needs to classify alternatives rather than rank them
- Predefined category boundaries exist (e.g., pass/fail, excellent/good/poor)
- Need to independently determine category membership for each alternative
Budget
| Base SOP | Target | ±10% Range |
|---|---|---|
| criterion-definition | 5-8 criteria | 4-9 |
| weight-elicitation-sop | 1 weight vector | 1 |
| threshold-setting | 1 threshold set | 1 |
| alternative-scoring | 1 score matrix | 1 |
| scoring-synthesis | 1 classification | 1 |
State Ledger
strategy: category-sorting
status: pending
categories_defined: false
criteria_defined: false
weights_computed: false
thresholds_set: false
scores_computed: false
classified: false
result: null
Available Tactics
- scoring-matrix-construction — Build scoring foundation
- screening-then-scoring — Hybrid workflow: screen first, then classify
Available SOPs
Import (from tactics)
- criterion-definition
- weight-elicitation-sop
- alternative-scoring
- threshold-setting
Subagent
- scoring-synthesis
Execution Guidance
- Define category definitions and boundary conditions
- Invoke criterion-definition to determine classification criteria
- Invoke threshold-setting to set category boundaries
- Score each alternative independently and determine category membership
- Handle borderline cases (pessimistic vs optimistic assignment)
Output Format
## Classification Results
**Method:** [ELECTRE-Tri / FlowSort / AHPSort / DRSA]
**Category Definitions:** [A=Excellent, B=Good, C=Needs Improvement, D=Unqualified]
### Classification Table
| Alternative | Category | Confidence | Boundary Distance |
|-------------|----------|------------|-------------------|
### Borderline Cases
[List alternatives near category boundaries and their sensitivity]
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 · 96 lines · 28 tokens per session scan A a8a62777894f
category-sorting is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (464 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 640 once invoked, about $0.0001 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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