code-change-impact

A procedure for tracing the wider effects of a code change and checking whether related features still work. It adapts to the project’s language and tools by first discovering its own build, test, and lint commands.

In plain words
What is it for?
Use it after changing a feature, function, module, type, route, API endpoint, schema, or configuration, before committing, opening a pull request, or pushing.
Why use it?
The most important breakages may be outside the file that was edited, such as callers, shared helpers, API clients, generated files, or other consumers.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/mghareeb/code-change-impact/code-change-impact
Any agent
npx skills add mghareeb/code-change-impact --skill code-change-impact
Clone the repo
git clone --depth 1 https://github.com/mghareeb/code-change-impact

Made for: Claude Code, Codex.

Per session 238 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,586 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00238 $0.02586
Opus 5 $0.00119 $0.01293
Sonnet 5 $0.00048 $0.00517
Haiku 4.5 $0.00024 $0.00259

Measured yesterday against content hash b3235f524874, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-change-impact 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 yesterday.

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.

SKILL.md · 213 lines

How it starts

The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Change Impact — blast-radius / regression analysis for any codebase

A fix is "done" only when you know what it touched besides the thing you were fixing. The dangerous ripples are rarely in the file you edited — they're in the callers of a function whose behavior you changed, the other modules that import a shared helper, the client that decodes a response whose shape you altered, the generated/duplicated twin file you forgot to regenerate, or a consumer that now fails to compile. This skill finds those, then proves the answer by running the project's own build/test/lint and exercising the impacted surfaces.

Run it after a change, against the VCS diff (the diff is the source of truth for "what changed"). It adapts to the language and tooling of whatever repo it's in — so Phase 0 (discovery) comes first and the rest builds on it.

Phase 0 — Discover the project (do this before analyzing)

You can't trace dependencies or verify a build until you know the project's conventions. Spend a minute learning them; cache the findings for the rest of the run. See references/recipes.md §1–2 for per-ecosystem detection commands.

Establish:

  1. Repo root + diffgit rev-parse --show-toplevel; then the change set (working tree, staged, or branch-vs-base). Use the VCS in play (git/hg/jj).
  2. Languages + ecosystems — from manifests and file extensions (package.json, pyproject.toml/requirements.txt, go.mod, pom.xml/ build.gradle, *.csproj/*.sln, Gemfile, Cargo.toml, composer.json). A repo may have several (e.g. a JS frontend + a Go backend).
  3. Verification commands — the project's own typecheck, build, test, and lint commands. Read them from the manifest's script section / Makefile / CI config rather than guessing (references/recipes.md §2). Note which test scripts are targeted (named per area) — those let you verify a specific blast radius cheaply.
  4. Module + import style — how this language names and imports modules, and any path aliases (e.g. a TS tsconfig.json paths, a Python namespace, a Go module path). This is what your reverse-dependency greps will key on.

Read the full file on GitHub · 213 lines

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. yesterday First seen · 213 lines · 238 tokens per session scan A b3235f524874

Subscribe to this mod's changes

code-change-impact is a skill published in the GitHub repository mghareeb/code-change-impact (2 stars, last pushed 2mo ago), licensed MIT. It adds 238 tokens to every session and 2,586 once invoked, about $0.0012 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.

Related

Other skills, from other repositories

karpathy-llm-wiki

Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.

Astro-Han/karpathy-llm-wiki · 67 tokens

nopua

The anti-PUA. Drives AI with wisdom, trust, and inner motivation instead of fear and threats. Activates on: task failed 2+ times, about to give up, suggesting user do it manually, blaming environment unverified, stuck in loops, passive behavior, or user frustration ('try harder', 'figure it out', '换个方法', '为什么还不行').…

wuji-labs/nopua · 90 tokens

nopua-lite

NoPUA Lite — core wisdom in 1.5k tokens. Drives AI with trust and inner motivation instead of fear. Same Daoist philosophy, minimal footprint. For personal use and small-context models.

wuji-labs/nopua · 46 tokens

resemble-detect

Deepfake detection and media safety — detect AI-generated audio, images, and video, trace synthesis sources, and analyze media intelligence using direct Resemble AI API calls.

resemble-ai/detect-skill · 38 tokens

ios-agent-skill

Expert iOS/Swift developer behavior for AI coding agents. Use when writing, reviewing, or refactoring Swift, SwiftUI, UIKit, or SwiftData code; when designing iOS app architecture (MVVM, Clean Architecture, coordinators, routing); when building UI that must meet Apple's Human Interface Guidelines, contrast, dark-mode…

Nagarjuna2997/ios-agent-skill · 167 tokens

cinematic-scroll

Build cinematic scroll-driven, 3D-tilt, parallax, and environment-morphing websites — pinned chapter reveals, hero parallax, depth-image figures, hover-tilt cards, background-morphing layouts, release/launch pages, product story pages, or editorial commerce microsites. From a single self-contained scroll section (Mode…

MustBeSimo/cinematic-scroll-skill · 301 tokens