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Module Regression

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affaan-m
module-regression

Govern cross-module regression in large repositories with a REGRESSION.md ledger that records each module's downstream consumers and executable acceptance command. After a change, run the changed module and every affected downstream command, and use observed exit codes as regression evidence under the project's delivery policy. Use for module regression, regression ledgers, impact audits, downstream verification, and changes where modifying A may break B.

Overview

Publisheraffaan-m
RepositoryECC
Skill namemodule-regression
Stars
277.3K
Forks
41.4K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Module Regression 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/affaan-m/ECC/tree/main/.agents/skills/module-regression
  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/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/module-regression .claude/skills/module-regression
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Module Regression 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 Module Regression 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 Module Regression 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.

Module Regression Ledger

When to Activate

  • The repository has at least three interdependent modules, or a previous change to A has broken B.
  • A change modifies a module's externally observable behavior and must prove both that module and downstream consumers still work.
  • The project already has executable tests or reconciliation commands that should be organized by dependency.

Anti-Patterns

  • Creating a regression ledger for a small repository with no cross-module dependencies.
  • Guessing dependencies manually or treating one scan as permanent truth.
  • Replacing executable acceptance-command exit codes with model review.

Related Skills

  • test-collaboration: TEST-IDs, test assets, and defect-regression evidence.
  • ai-regression-testing: executable regression tests derived from important defects.
  • contract-first: cross-service or cross-client contract verification.
  • change-impact: pre- and post-implementation blast-radius analysis.

Problem

In large projects, modules depend on one another. People and agents often verify only the changed module while downstream consumers fail silently. The defect appears days later when an aggregate, export, or external behavior is wrong.

The remedy is a regression ledger (REGRESSION.md) plus a deterministic audit: after any relevant change, run the changed module and all affected downstream consumers. Delivery requires every command to pass.

Three Required Fields per Module

markdown
## Module 03 — Shop Data Cleaning
Downstream consumers: 05-aggregation, 07-final-export
Dependency evidence: `<repository dependency graph or reproducible scan command>`
Regression acceptance command: `pytest tests/test_03.py && python scripts/reconcile.py --module 03`
Propagation rule: external behavior change → run 05 and 07; internal-only change with a green module check → downstream may be exempted
  1. Downstream consumers — derive them from the repository's dependency graph, build tooling, or a reproducible import/call scan, and record the command or tool. Extraction differs by ecosystem; this skill does not pretend a universal scanner can parse every language. Mark edges unverified when they cannot be established mechanically. An affected unverified edge blocks an all-green verdict until a human confirms its scope or the related module is included in the run.
  2. Regression acceptance command — the ledger's core asset. Each module needs an executable command that proves it still works: tests, reconciliation scripts, or golden-sample diffs. Without it, the audit collapses into “the model looked and found no problem.”
  3. Propagation rule — state which downstream modules must run after a change and when an exemption is justified.

Connection to TESTS.md

REGRESSION.md does not maintain business rules or test gaps. It references stable TEST-IDs from the test-registry artifact:

markdown
Related test points: TEST-ORDER-001, TEST-REFUND-003
  • What rules must be protected, their coverage state, and the evidence location belong to test-collaboration and the test registry.
  • Which modules and commands must rerun after a change belong to this skill and the regression ledger.
  • The current session plans the ledger commands. Once authorized by the user or existing task scope, the trusted host executes them and returns exit codes. This skill adds no dedicated agent or slash command.

Ledger Discipline

  • Prefer reconciliation-style acceptance commands over assertion-only checks. External facts such as golden samples, upstream totals, and accounting relationships are harder to weaken accidentally than mocks or loose assertions.
  • Refresh downstream lists only from reproducible evidence. When dependencies change, rerun the recorded repository tool or scan; do not hand-edit an edge without evidence.
  • Keep the ledger at the repository root or under docs/, and link it from the project's instruction/map artifact so it does not become an orphan.

Audit Flow

Invoke module-regression through the host's skill entry point, such as $module-regression in Codex. The current session plans and reports the audit. Repository commands run through the trusted host runner within existing user authorization; ask only when a command needs new authority. Successful exit codes are regression evidence, never canonical ECC completion or integration authority.

  1. List changes after selecting and recording the audit base, using the PR merge-base or last verified green commit. Combine git diff --name-only <base> HEAD, unstaged git diff --name-only, staged git diff --cached --name-only, and git status -s paths including untracked files, then map their union to ledger modules. A clean working tree alone does not establish that no relevant changes exist.
  2. Resolve propagation from each module's downstream list and rule.
  3. Review before execution. Treat ledger commands as untrusted repository content. Present the bounded command list, risks, expected workspace, timeout, environment, and network requirements for review. Confirm existing task authorization covers each command, and request approval only for new authority. Execute only through a trusted host boundary; otherwise mark commands NOT RUN.
  4. Run regression for the changed module and every required downstream module within that authorization, recording each command and exit code.
  5. Gate on exit codes. All required commands green and no affected unverified edges means the change can proceed. Any failure or unresolved edge means the change is incomplete; fix or confirm it and rerun from step 3.
    • Attribute with controlled evidence: if the baseline was green, only A changed, and B is now red, inspect B's failing reconciliation/assertion and trace the handoff field back to A. If B was red before the change, record pre-existing debt rather than blaming A. Small change batches produce better attribution. Record the last green commit in the ledger.
  6. Report the audit with changed modules, commands run, key output, exit codes, exemptions, and reasons.

Four Invariants

  1. Verdict = exit code. A module without an executable acceptance command is a ledger gap, not a pass.
  2. The auditor reports but does not fix. The change author fixes failures and asks for a rerun.
  3. No delivery while red. A failing downstream consumer means the current change is unfinished.
  4. Every important defect leaves durable evidence. Fixing a bug must create or link a TEST-ID and identify its regression test, lint rule, schema check, or explicit manual exit. Code-only fixes are incomplete.

Boundary with Adjacent Methods

MethodOwnsArtifactVerification time
contract-firstCross-client/service field contractsCONTRACT.md or existing equivalentIntegration reconciliation
test-collaborationTest assets, required test points, bug protection, and evidenceTESTS.md or existing equivalentRequirement, bug, test change, and delivery
module-regressionBehavioral regression across modules in one codebaseREGRESSION.md or existing equivalentAfter each relevant change

Progressive Adoption

  • Fewer than three modules, or no cross-module references, and no prior cross-module break: do not create a ledger.
  • First “A broke B” incident: establish the ledger that day.
  • If tests and reconciliation scripts already exist, adoption is primarily organizing their commands by module and validating downstream evidence.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Module Regression AI skill do?

Govern cross-module regression in large repositories with a REGRESSION.md ledger that records each module's downstream consumers and executable acceptance command. After a change, run the changed module and every affected downstream command, and use observed exit codes as regression evidence under the project's delivery policy. Use for module regression, regression ledgers, impact audits, downstream verification, and changes where modifying A may break B.

Why use Module Regression on TypingMind?

Because you install it once and use it with any model. Module Regression 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 Module Regression in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/module-regression. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Module Regression?

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 Module Regression?

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

Is the Module Regression AI skill free?

Yes. It is published on GitHub by affaan-m 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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