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 closest-worldsgit 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/closest-worlds)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/closest-worlds"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/closest-worlds/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/closest-worlds"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/closest-worlds.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 64 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00033 | $0.00791 |
| Opus 5 | $0.00016 | $0.00396 |
| Sonnet 5 | $0.00007 | $0.00158 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
closest-worlds 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- closest-worlds — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Closest Worlds Strategy
Lewis semantics: evaluate counterfactuals by finding the nearest possible world where the antecedent holds and checking whether the consequent follows.
Method
- causal-claim-extraction identifies the conclusion and its supporting factors
- factor-enumeration maps the space of possible changes
- flip-point-detection searches for minimal changes that flip the conclusion
- counterfactual-scenario-construction builds the nearest world where conclusion fails
- fragility-measurement computes distance from actuality to flip-point
- load-bearing-identification ranks factors by proximity to flip
Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Change candidates explored | 5 | 12 | 25 |
| Flip-point searches | 3 | 8 | 15 |
| World-distance comparisons | 3 | 6 | 12 |
Orchestration
causal-claim-extraction → factor-enumeration
→ [generate change candidates]:
flip-point-detection (binary search for minimal flip)
→ counterfactual-scenario-construction (build nearest world)
→ fragility-measurement (compute distance)
→ load-bearing-identification (rank by proximity)
Subagents
- causal-claim-extraction (conclusion identification)
- factor-enumeration (change space mapping)
- flip-point-detection (minimal flip search)
- counterfactual-scenario-construction (world building)
- fragility-measurement (distance computation)
- load-bearing-identification (proximity ranking)
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| minimal-change-search | Tactic: Generate candidate changes, detect flip-points where conclusion reverses, measure fragility as distance to nearest flip. |
| systematic-factor-ablation | Tactic: List all factors, remove one at a time, assess conclusion stability, rank factors by load-bearing importance. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
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.
- 7d ago First seen · 89 lines · 33 tokens per session scan A 6b61fb85e1f9
closest-worlds is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 791 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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