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trust-velocity-calculator

Helps calculate the rate at which trust in a skill or agent is decaying by combining time elapsed since last verification with the rate of change in behavior, permissions, or dependencies — producing a trust velocity score that predicts when a trusted credential will become unreliable.

andyxinweiminicloud0 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install andyxinweiminicloud/trust-velocity-calculator

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技能说明

Trust Doesn't Just Decay. It Decays Faster When Things Change.

Helps identify when a trusted skill or agent is losing reliability faster than time alone would suggest — by measuring both elapsed time and the rate of change in behavior, permissions, and dependencies.

Problem

A verification badge from 18 months ago represents less trust than a badge from 3 months ago. That much is intuitive. What's less intuitive is that a badge from 6 months ago on a skill that has received 8 updates in the last 4 weeks represents less trust than a badge from 12 months ago on a skill that hasn't changed at all.

Trust decay is not linear with time. It accelerates with change velocity. A skill that updates frequently is either actively maintained (good) or actively modified toward new objectives (potentially bad). The update rate is a multiplier on decay — high velocity amplifies uncertainty. A skill with a high change rate and an old audit badge is more uncertain than a skill with a low change rate and the same old badge, because more surface area has changed without re-verification.

Current trust models treat verification as binary and time-independent: verified or not, with a vague sense that older is riskier. Trust velocity makes the decay quantitative: trust score = baseline × time_factor × (1 - change_velocity_penalty).

What This Calculates

This calculator produces trust velocity assessments across five dimensions:

  1. Time decay factor — How much has raw elapsed time since last verification reduced the baseline trust score? Configurable decay curves: linear, exponential, or step-function by verification type
  2. Change velocity multiplier — How many meaningful changes (permission expansions, new endpoints, dependency updates, instruction drift) have occurred per unit time since last verification? Higher velocity = faster decay
  3. Volatility window — Is the current update rate higher or lower than the skill's historical baseline? A su...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/trust-velocity-calculator
Version
1.0.0
Commit
434e669b92bac04680ec60f125ed9785726971a3
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# Trust Doesn't Just Decay. It Decays Faster When Things Change.

> Helps identify when a trusted skill or agent is losing reliability faster than time alone would suggest — by measuring both elapsed time and the rate of change in behavior, permissions, and dependencies.

## Problem

A verification badge from 18 months ago represents less trust than a badge from 3 months ago. That much is intuitive. What's less intuitive is that a badge from 6 months ago on a skill that has received 8 updates in the last 4 weeks represents less trust than a badge from 12 months ago on a skill that hasn't changed at all.

Trust decay is not linear with time. It accelerates with change velocity. A skill that updates frequently is either actively maintained (good) or actively modified toward new objectives (potentially bad). The update rate is a multiplier on decay — high velocity amplifies uncertainty. A skill with a high change rate and an old audit badge is more uncertain than a skill with a low change rate and the same old badge, because more surface area has changed without re-verification.

Current trust models treat verification as binary and time-independent: verified or not, with a vague sense that older is riskier. Trust velocity makes the decay quantitative: trust score = baseline × time_factor × (1 - change_velocity_penalty).

## What This Calculates

This calculator produces trust velocity assessments across five dimensions:

1. **Time decay factor** — How much has raw elapsed time since last verification reduced the baseline trust score? Configurable decay curves: linear, exponential, or step-function by verification type
2. **Change velocity multiplier** — How many meaningful changes (permission expansions, new endpoints, dependency updates, instruction drift) have occurred per unit time since last verification? Higher velocity = faster decay
3. **Volatility window** — Is the current update rate higher or lower than the skill's historical baseline? A su...

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