flip-point-detection

flip-point-detection is a skill for Claude Code, Codex from yogsoth-ai/stress-test. It costs 24 tokens per session (312 once invoked), scanned A, original, Apache-2.0.

An analysis method that finds the smallest change to an input that reverses a conclusion. It uses repeated tests between the original state and a changed state.

In plain words
What is it for?
Use it to test how much a design, decision, or other artifact can change before its conclusion flips, and to record the search path and confidence.
Why use it?
It shows how close a conclusion is to changing, instead of only recording whether the original conclusion is true.

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 how much a design, decision, or other artifact can change before its conclusion flips, and to record the search path and confidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yogsoth-ai/stress-test/flip-point-detection
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 flip-point-detection
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 flip-point-detection

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/flip-point-detection.svg)](https://agentmods.dev/skills/yogsoth-ai/stress-test/flip-point-detection)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/flip-point-detection"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/flip-point-detection.svg" alt="Measured on agentmods" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 312 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 $0.00024 $0.00312
Opus 5 $0.00012 $0.00156
Sonnet 5 $0.00005 $0.00062
Haiku 4.5 $0.00002 $0.00031

Measured 8d ago against content hash e6cb91dffa31, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

flip-point-detection 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 8d 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/flip-point-detection/SKILL.md · 53 lines

What it actually says

Flip-Point Detection

Binary search for the minimal perturbation that reverses the conclusion.

Execution

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

Why Subagent

Flip-point detection requires iterative reasoning about graduated changes, best done in isolated context.

Input

  • artifact: The original artifact
  • dimension: The dimension to perturb along
  • conclusion: The conclusion being tested

Output

  • flip_point: The minimal change that flips the conclusion
  • distance: How far from actuality the flip-point is (0.0–1.0)
  • confidence: Confidence in the flip-point location
  • search_path: Steps taken to find the flip-point

Budget

One unit = one binary search per dimension.

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. 8d ago First seen · 53 lines · 24 tokens per session scan A e6cb91dffa31

Subscribe to this mod's changes

flip-point-detection is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 312 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 2026-08-31.

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