Risky Changes

CommunityPopular

davidondrej

risky-changes

Verify assumptions before implementing large or risky changes to APIs, provider data, billing, pricing, quotas, or defaults. Use when a mistake could affect customers or the user asks if a change is safe to ship. Checks real-world impact beyond passing tests.

Overview

Publisher	davidondrej
Repository	`skills`
Skill name	`risky-changes`
Stars	4.1K
Forks	599
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by davidondrej on GitHub. Read the source before you install it.

Installation

Install the Risky Changes AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/davidondrej/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/ops-and-setup/risky-changes .claude/skills/risky-changes

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Risky Changes in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Risky Changes on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Risky Changes is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Risky Changes

A filter once passed every test but disabled ~99% of the feature on live data. Tests check code correctness; research and live measurement check whether a change is useful.

When this applies

Use for changes where being wrong is expensive or customer-visible:

Public API fields, filters, or response shaping
Dropping, transforming, or reordering upstream data
Billing, pricing, caps, or quotas
Defaults, thresholds, or provider request parameters
Unverified assumptions about external data or user behavior

If unsure whether a change qualifies, apply this process.

1. Identify assumptions

List the assumptions the change depends on. Mark which have evidence and which are still unverified.

2. Research before implementing

Use available research tools and reliable sources. Investigate each distinct question separately, covering at least:

How do leading products handle this design decision?
What does real-world data look like: frequencies, shapes, and edge cases?
What do users or agents actually need?

If evidence contradicts an assumption, reconsider the design before coding. If research is unavailable or inconclusive, state the gap; do not treat the assumption as verified. Do not ship while material assumptions remain unverified.

3. Measure real behavior

Run 10–20+ realistic cases against the real endpoint or provider:

Base cases on real usage; vary topics, parameters, languages, and edge conditions.
Define benchmarks per case: speed, quality, accuracy, and how often the new behavior occurs.
Use hard numbers where possible. For subjective quality, use blind, criteria-based judging.
Compare before and after when both can be measured.
Read-only production analysis also counts as measurement.

Save the cases and results in the project's evals folder, e.g. docs/evals/YYYY-MM-DD-<endpoint>-<focus>.md. Create the folder if needed. Without this record, the change is not verified. Unit tests do not replace live measurement.

4. Confirm product-owner approval before shipping

Present decisions that affect what customers see or pay, with supporting research and measurements. Get the product owner's approval for those decisions; do not bury them in a plan or code default. Existing approval counts if it covers the actual behavior being shipped.

5. Verify after deployment

Within one day, measure the change on real traffic through read-only production analysis or a live sweep. Report results that differ from expectations immediately.

Frequently asked questions

What does the Risky Changes AI skill do?

Why use Risky Changes on TypingMind?

Because you install it once and use it with any model. Risky Changes is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Risky Changes in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/davidondrej/skills/tree/main/skills/ops-and-setup/risky-changes. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Risky Changes?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Risky Changes?

As many as you like. As long as a model supports skills, you can use Risky Changes with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Risky Changes AI skill free?

Yes. It is published on GitHub by davidondrej under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Risky Changes

When this applies

1. Identify assumptions

2. Research before implementing

3. Measure real behavior

4. Confirm product-owner approval before shipping

5. Verify after deployment

Frequently asked questions

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