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Blast Radius

CommunityPopular
pedrohcgs
blast-radius

Before and after changing anything shared — a function's return value, a signature, a schema, a label set, a config default, a constant, a file format — find every consumer and actually run them. Catches the change that looks purely additive but silently breaks a contract in a file you never opened. Use when editing shared code, adding a field/column/return element, renaming, changing units or defaults, or touching a pipeline that produces reported numbers.

Overview

Publisherpedrohcgs
Repositoryclaude-code-my-workflow
Skill nameblast-radius
Stars
1.6K
Forks
3.1K
Bundled files
Instructions only
LicenseMIT
Links
  • 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 pedrohcgs on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/blast-radius
  4. 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.
2

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/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p .claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/skills/blast-radius .claude/skills/blast-radius
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blast Radius 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 Blast Radius 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 Blast Radius 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.

Know the blast radius before you change it

The dangerous change is not the risky-looking one. It is the one that looks purely additive — adding a returned value, a column, an option — and quietly violates a contract three files away that nobody re-read. Compilation and type checks will not catch a positional or length contract; you get either a crash far from the edit, or worse, silently wrong output.

Rule: if you change a shared interface, run its consumers. Reading them is not running them.

1. Enumerate consumers before editing

Grep for every call site, import, and downstream reference — including tests, notebooks, scripts, docs, and anything that regenerates reported results. Note which ones produce numbers that appear in a paper, dashboard, or release: those are the ones where silent breakage is most costly.

If a consumer lives in another repo, another language, or a generated artifact, write it down now; you will not remember at verification time.

A consumer in another repo pins this one by commit SHA. Its verification receipt records the revision it was built against — not a branch, not a version string, both of which keep moving under it. So a change here that moves a number the downstream reports is not finished when this repo goes green: before/after evidence for what moved, regeneration of the downstream artifact, and the re-pin all belong to the same round as the change — release-engineering.md §6 has the ordering within it. A downstream left pinned to the old SHA is an honest, inspectable state; one pointed at a moving reference silently inherits a number nobody re-verified.

2. Name the contract you are about to change

Ask explicitly what downstream code is entitled to assume:

  • Arity / length — does anything index positionally, zip against a fixed list, or preallocate a matrix of known width? Adding an element breaks all three.
  • Names and order — does anything match by name, by position, or pair your output against a separate parallel list of labels?
  • Types, units, scale — dollars vs cents, rate vs percent, seconds vs ms, 0-indexed vs 1-indexed.
  • Nullability and sentinels — new empty/NA cases a consumer will not expect.
  • Defaults — changing a default silently changes every caller that relied on it.
  • Identity/ordering guarantees — row order, sort stability, key uniqueness.

The classic failure: a returned vector grows from 6 to 7, while a consumer pairs it against a hard-coded list of 6 labels. Nothing errors at the edit site; the consumer either throws far away or, worse, recycles and mislabels every row.

3. Prefer changes that cannot break a contract

Additive-and-named beats additive-and-positional. Where you control the consumer, match by name rather than position. Where you cannot, version the interface rather than widening it in place.

Do not "fix" a mismatch by deriving labels/config from the new data if the old labels were deliberately different — deliberate relabeling exists (display names differing from internal names), and auto-deriving silently changes published output.

4. Run the consumers — end to end, on real inputs

A consumer that merely imports is not exercised. Run at least one full path per distinct consumer pattern, and prefer the one that regenerates reported numbers.

Then verify both directions:

  • The new thing works.
  • The old things are unchanged. Diff previously-reported outputs; anything that moved must have a reason you can state. If the change was supposed to be behavior-preserving, byte-identical or within a declared tolerance is the evidence — not "it ran".

5. Green is uninformative if nothing ran

Confirm the check actually executed and could have failed: a skipped test, a filtered-out case, an exception swallowed into a default, or a tolerance widened after the comparison are all indistinguishable from success in a log. Where the change is consequential, seed a defect and confirm the check goes red — a comparison that cannot fail is not evidence.

6. Record the contract change

If the interface genuinely changed, say so where consumers will look: a NEWS/CHANGELOG entry, a versioned interface note, or a comment at the definition naming what downstream code may assume. For anything reused or released, freeze inputs (versions, hashes, seeds) and record declared tolerances so the next comparison is reproducible rather than renegotiated.

Minimum checklist

  1. Grep all consumers, including tests, scripts, docs, other repos/languages.
  2. Write down the contract: arity, names, order, types, units, defaults, ordering.
  3. Make the change name-based/versioned where you can.
  4. Run at least one full path per consumer pattern.
  5. Diff previously-reported outputs; explain any movement.
  6. Seed a defect to prove the check can fail.
  7. Record the contract change where consumers will see it.
  8. Re-pin every cross-repo consumer to the new SHA — regenerated and re-verified in this round, not the next one.

Cross-references

Frequently asked questions

What does the Blast Radius AI skill do?

Before and after changing anything shared — a function's return value, a signature, a schema, a label set, a config default, a constant, a file format — find every consumer and actually run them. Catches the change that looks purely additive but silently breaks a contract in a file you never opened. Use when editing shared code, adding a field/column/return element, renaming, changing units or defaults, or touching a pipeline that produces reported numbers.

Why use Blast Radius on TypingMind?

Because you install it once and use it with any model. Blast Radius 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 Blast Radius in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/blast-radius. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Blast Radius?

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 Blast Radius?

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

Is the Blast Radius AI skill free?

Yes. It is published on GitHub by pedrohcgs 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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