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Foreman To Issues

Organization
VisionForge-OU
foreman-to-issues

Break an approved PRD into small, dependency-ordered, vertically-sliced implementation issues written as local files in the Foreman feature directory. Each issue ships a runnable acceptance check and a declared file footprint. No GitHub, no live quizzing of the user — emits files matching Foreman's issue schema with PRD traceability.

Overview

PublisherVisionForge-OU
Repositoryforeman
Skill nameforeman-to-issues
Stars
444
Forks
117
Bundled files
Instructions only
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 VisionForge-OU on GitHub. Read the source before you install it.

Installation

Install the Foreman To Issues 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/VisionForge-OU/foreman/tree/main/src/foreman/skills/foreman-to-issues
  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/VisionForge-OU/foreman.git /tmp/foreman
mkdir -p .claude/skills
cp -r /tmp/foreman/src/foreman/skills/foreman-to-issues .claude/skills/foreman-to-issues
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Foreman To Issues 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 Foreman To Issues 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 Foreman To Issues 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.

foreman-to-issues

(Adapted from mattpocock/skills to-issues — see NOTICE. Removed: all gh CLI usage, GitHub labels/triage vocabulary, and the interactive "quiz the user" loop. Issues are emitted as local files; the human reviews them in Foreman's queue-review screen instead.)

Break the approved PRD into independently-grabbable issues using vertical slices (tracer bullets). Run headless: produce the files and stop. The human will reorder/edit/delete/add in Foreman's queue-review screen — do not ask them anything here.

Process

1. Gather context

Read the approved prd.md (path injected by Foreman) — its body, user stories, and user flows. Read the approved plan.md and adr.md too for decisions.

2. Explore the codebase

Understand the current state so titles and descriptions use the project's domain glossary (CONTEXT.md) and respect ADRs in the area you're touching.

3. Draft vertical slices

Break the PRD into tracer-bullet issues. Each issue is a thin vertical slice that cuts through ALL integration layers end-to-end (schema → logic → API → UI → tests), NOT a horizontal slice of one layer.

4. Emit one file per slice

Write each slice to .foreman/features/<slug>/issues/ISS-NNN.md, numbered from 001 in dependency order (blockers first, so depends_on can reference real ids). Each file is YAML frontmatter + markdown body in exactly this schema:

md
---
id: ISS-001
title: <short descriptive name>
status: queued
depends_on: []            # list of blocking issue ids, e.g. ["ISS-001"]
branch: feature/<slug>/iss-001
attempts: 0
budget: { max_turns: 80, max_cost_usd: 5.00, timeout_min: 45 }
prd_refs: ["PRD §<section>", "Story #<n>"]   # traceability back to the PRD
acceptance_check: tests/<area>/test_<slice>.py   # runnable check (REQUIRED)
touches: ["src/<area>/...", "tests/<area>/..."]  # declared file footprint
kind: feature
---
## Goal

A concise description of this vertical slice — the end-to-end behavior, not a
layer-by-layer implementation plan. Avoid file paths and code snippets (they go
stale); a prototype-derived decision snippet may be inlined if it encodes a
decision more precisely than prose.

## Acceptance criteria (testable)

- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3

## Out of scope

- What this slice deliberately does not do.

Rules:

  • id is ISS- + zero-padded three-digit number, unique within the feature.
  • branch is feature/<slug>/iss-NNN (lowercase).
  • budget defaults come from the feature's config run_budget; only deviate when a slice is clearly bigger or smaller, and say why in the body if you do.
  • prd_refs MUST be present and non-empty — every issue traces to the PRD.
  • depends_on MUST be acyclic and reference only earlier issues.

acceptance_check — the executable form of acceptance (REQUIRED)

Every issue MUST carry a runnable acceptance_check derived directly from a PRD acceptance criterion, so "done" is executable, not prose. It is either:

  • a bare test-file path (Foreman runs it with the project's test command), e.g. acceptance_check: tests/todo/test_done.py; or
  • a command (more than one token), e.g. acceptance_check: "pytest tests/todo/test_done.py::test_marks_complete".

If the check is a dedicated test the slice must add, write that test file into a canonical issues/ISS-NNN.check/ directory (same relative path it should have in the repo, e.g. issues/ISS-001.check/tests/todo/test_done.py). Foreman re-installs it into the worktree before verifying so the worker cannot weaken or delete it. An issue with no acceptance_check is rejected from the queue.

touches — the declared file footprint (for safe parallelism)

List the files/dirs/modules the slice will create or modify. Foreman builds a conflict graph from these and never runs two overlapping issues concurrently, so maximise disjointness when slicing — prefer slices whose footprints don't overlap. An empty/unknown footprint is treated as conflicting with everything (the safe default), which serialises the slice — so always declare it.

Do not create any external tickets. Do not modify the PRD.

Frequently asked questions

What does the Foreman To Issues AI skill do?

Break an approved PRD into small, dependency-ordered, vertically-sliced implementation issues written as local files in the Foreman feature directory. Each issue ships a runnable acceptance check and a declared file footprint. No GitHub, no live quizzing of the user — emits files matching Foreman's issue schema with PRD traceability.

Why use Foreman To Issues on TypingMind?

Because you install it once and use it with any model. Foreman To Issues 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 Foreman To Issues in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VisionForge-OU/foreman/tree/main/src/foreman/skills/foreman-to-issues. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Foreman To Issues?

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 Foreman To Issues?

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

Is the Foreman To Issues AI skill free?

It is published on GitHub by VisionForge-OU. Check the repository for licensing terms. 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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