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 factor-removalgit 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/factor-removal)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/factor-removal"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/factor-removal/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/factor-removal"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/factor-removal.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 | $0.00032 | $0.00764 |
| Opus 5.5 | $0.00013 | $0.00306 |
| Sonnet 5.5 | $0.00006 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
factor-removal 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 1mo 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factor Removal Strategy
Ablation study approach: systematically remove each factor and observe conclusion stability.
Method
- factor-enumeration lists all factors supporting the conclusion
- single-factor-removal removes one factor at a time
- counterfactual-scenario-construction reasons about the modified scenario
- fragility-measurement scores how much the conclusion degrades
- load-bearing-identification ranks factors by impact of removal
- Optional: flip-point-detection for partial removal (dose-response)
Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Factors removed | 5 | 10 | 20 |
| Removal iterations | 1 | 2 | 3 |
| Combination removals | 0 | 3 | 8 |
Orchestration
factor-enumeration → [rank by suspected importance]
→ [for each factor]:
single-factor-removal
→ counterfactual-scenario-construction
→ fragility-measurement
→ [if budget allows, test combinations]:
single-factor-removal (multiple factors)
→ counterfactual-scenario-construction
→ load-bearing-identification (final ranking)
Subagents
- factor-enumeration (factor listing)
- single-factor-removal (ablation)
- counterfactual-scenario-construction (scenario reasoning)
- fragility-measurement (degradation scoring)
- load-bearing-identification (ranking)
- flip-point-detection (dose-response, optional)
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.
| SOP | When to use |
|---|---|
| counterfactual-scenario-construction | Construct precise, internally consistent counterfactual scenarios where specified factors are altered, then reason about the resulting conclusion. |
| factor-enumeration | List all key factors, conditions, and assumptions that support or enable the artifact's conclusion. |
| flip-point-detection | Find the minimal change magnitude along a dimension that causes the conclusion to flip from true to false. |
| fragility-measurement | Compute a fragility index from flip-point distances and degradation scores, summarizing how robust the conclusion is. |
| load-bearing-identification | Identify which factors are "load-bearing walls" — factors whose removal would collapse the conclusion. |
| single-factor-removal | Remove one specified factor from the artifact's support structure and reason about how the conclusion 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.
- 1mo ago First seen · 91 lines · 32 tokens per session scan A 5989ee2eef15
factor-removal is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 764 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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