single-factor-removal

single-factor-removal is a skill for Claude Code, Codex from yogsoth-ai/stress-test. It costs 23 tokens per session (319 once invoked), scanned A, original, Apache-2.0.

An ablation method that removes one selected factor from an argument or other piece of work and checks how much the conclusion changes.

In plain words
What is it for?
Use it to test the stability of conclusions by examining the result with individual factors removed.
Why use it?
It shows whether a conclusion depends heavily on a particular factor. This helps distinguish essential support from details that have little effect.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to test the stability of conclusions by examining the result with individual factors removed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yogsoth-ai/stress-test/single-factor-removal
Install

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.

Any agent
npx skills add yogsoth-ai/stress-test --skill single-factor-removal
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/stress-test

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for single-factor-removal

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/single-factor-removal/github.svg)](https://agentmods.dev/skills/yogsoth-ai/stress-test/single-factor-removal)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/single-factor-removal"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/single-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.

agentmods 80×15 button for single-factor-removal

Your own site · 80×15
<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/single-factor-removal"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/single-factor-removal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 319 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.00319
Opus 5 $0.00012 $0.00160
Sonnet 5 $0.00005 $0.00064
Haiku 4.5 $0.00002 $0.00032

Measured 7d ago against content hash 1b90814979d7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

single-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 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.

skills/single-factor-removal/SKILL.md · 53 lines

What it actually says

Single Factor Removal

Ablation unit: removes one factor and reasons about conclusion stability.

Execution

Subagent — spawned via subagent-spawning/spawn-agent.

Why Subagent

Each removal must be reasoned about independently to avoid contamination from other removal results.

Input

  • artifact: The original artifact
  • factor_to_remove: Which factor to remove
  • factors_list: All factors (for context of what remains)

Output

  • conclusion_before: Original conclusion status
  • conclusion_after: Conclusion status without this factor
  • degradation_score: 0.0 (no effect) to 1.0 (collapse)
  • reasoning: Why the conclusion is/isn't affected

Budget

One unit = one factor removal per invocation.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP When to use
spawn-agent Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent.
Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

  1. 7d ago First seen · 53 lines · 23 tokens per session scan A 1b90814979d7

Subscribe to this mod's changes

single-factor-removal is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 319 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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