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/stress-test --skill closest-worldsgit clone --depth 1 https://github.com/yogsoth-ai/stress-testWrote 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/stress-test/closest-worlds)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/closest-worlds"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/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/stress-test/closest-worlds"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/closest-worlds.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 10d 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.
This is a copy
100% identical to closest-worlds — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 10d ago First seen · 89 lines · 33 tokens per session scan A 6b61fb85e1f9
closest-worlds is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), 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. It is 100% identical to closest-worlds, differing in 0 lines, and is treated as a copy.
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