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Plan Release

Community
danielvm-git
plan-release

RELEASE-INDEX BUILDER — Sequence elaborated epics into specs/release-plan.yaml with WSJF ordering and BCP baselines. NOT a planning-spine substitute: it does not scope work (scope-work) or write story tasks (plan-work). Use after elaborate-spec when the user wants a versioned release index of epics.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill nameplan-release
Stars
206
Forks
18
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 danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Plan Release 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.
  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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/plan-release .claude/skills/plan-release
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Release 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 Plan Release 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 Plan Release 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.

Plan Release

HARD GATE — Do NOT run this skill unless elaborate-spec has produced a clear spec or the user has already defined the feature in detail. If the problem is still fuzzy, run elaborate-spec first. HARD GATEspecs/product/SCOPE_LATEST.yaml (or legacy specs/product/SCOPE_LATEST.yaml) must exist. If missing, run scope-work first.

Synthesize the conversation context into specs/release-plan.yaml (index) and shard detail under specs/epics/. No new interview — only clarify if something is genuinely ambiguous.

Outputs

FileContent
specs/release-plan.yamlrelease.version, semver bump hint, WSJF-ordered epic list with id, capsule_dir, wsjf, bcpsno story status
specs/epics/eNN-<slug>/epic.yamlEpic manifest: id, title, wsjf, total_bcps, status, stories[] list
specs/epics/eNN-<slug>/eNNsYY-<slug>.mdStory spec in countable-story-format.md with 20 sections and Gherkin acceptance criteria
specs/epics/eNN-<slug>/eNNsYY-tasks.yamlDecoupled task checklist with verify: commands per task
specs/execution-status.yamlFlat key-value store for story status (eNNsYY: todo)

Epic Capsule Structure

All epics use capsule directories (no flat/folder distinction):

specs/epics/e01-auth-system/
├── epic.yaml              # Epic manifest
├── adr/                   # Epic-local ADRs (created lazily)
├── e01s01-login.md        # Story spec (countable-story-format)
├── e01s01-tasks.yaml      # Decoupled task checklist
├── e01s02-jwt.md          # Story spec
└── e01s02-tasks.yaml      # Decoupled task checklist

Rationale: Capsule dirs achieve change isolation (C9), enable archive pruning (C2/C6), and enforce SRP by decoupling spec .md from execution -tasks.yaml (C1).

Process

1. Draft epics and stories

From the conversation context, define:

  • Epicse01, e02, … (stable IDs; WSJF order in release-plan.yaml only)
  • Storiese01s01, e01s02, … with Gherkin acceptance criteria

WSJF-sort epics: score = (Business Value + Time Criticality + Risk Reduction) / Job Size. Highest score first.

Security risk boost: If an epic's specs/security/epics/<id>/THREAT_MODEL.md identifies HIGH or CRITICAL risk, add +2 to the WSJF numerator (BV + TC + RR + 2) to reflect the urgency of addressing security concerns before they ship. Document the boost in the epic's note field in release-plan.yaml.

2. Write acceptance criteria (Gherkin)

For each story, write at least one happy-path and one edge-case scenario (countable format §17 if maturity ≥ 3).

3. Write tasks with verify commands

Every task must have a verify: command. No verify command = not a task.

4. Save specs/release-plan.yaml

Do NOT hand-track the real version. semantic-release decides it at merge. version here is a non-authoritative mirror/label only — read the real number with gh release view. Set bump_hint (the expectation), not a number you intend to enforce.

yaml
release:
  version: "2.29.0"         # mirror of next expected tag, NOT authoritative — gh release view wins
  codename: "Feature Name"
  status: planning          # planning | in_progress | released
  semantic_release: true
  bump_hint: minor          # patch | minor | major — CI decides at merge
epics:
  - id: e01
    title: Auth System
    wsjf: 4.5
    capsule_dir: epics/e01-auth-system
  - id: e02
    title: User Profile
    wsjf: 3.8
    capsule_dir: epics/e02-user-profile

5. Save epic manifest (epic.yaml)

Each epic capsule directory contains an epic.yaml manifest:

yaml
id: e01
title: Auth System
wsjf: 4.5
total_bcps: 8
status: in_progress
stories:
  - id: e01s01
    title: Login
    bcps: 3
    status: todo
    spec: e01s01-login.md
    tasks: e01s01-tasks.yaml
  - id: e01s02
    title: JWT Token Management
    bcps: 5
    status: todo
    spec: e01s02-jwt.md
    tasks: e01s02-tasks.yaml

6. Save story specs (countable-story-format .md)

Each story becomes a standalone .md file following countable-story-format.md. Minimum: maturity 3 (Countable) with all 20 sections present. Acceptance criteria in §17 use Gherkin scenarios.

7. Save decoupled task files (-tasks.yaml)

Each story has a decoupled -tasks.yaml with implementation steps:

yaml
story_id: e01s01
title: Login
status: todo
bcps: 3
tasks:
  - id: 1
    description: "Add login form component tests"
    verify: "npm test -- login-form.test.tsx"
    status: todo
  - id: 2
    description: "Implement login form with validation"
    verify: "npm test -- login-form.test.tsx"
    status: todo

HARD GATE — Every task MUST have a runnable verify: command. No verify: = not a task.

→ verify: bash scripts/validate-specs-yaml.sh

7b. Generate bug registry summary

Read specs/bugs/registry.yaml and add a bugs: section to release-plan.yaml with totals by status (fixed, deferred, wontfix, open): bugs: { total: N, fixed: N, deferred: N, wontfix: N, registry: specs/bugs/registry.yaml }.

8. Sync execution status

bash
bash scripts/sync-status-from-epics.sh

9. Snapshot on planning close (optional)

Copy to specs/product/snapshots/release-<version>/ when the user approves the plan.

10. Suggest next steps

  • Run assess-impact before plan-work for any story touching existing modules.
  • Run plan-work per story for detailed steps inside the epic shard.
  • Run change-request if a new requirement arrives mid-flight.

Frequently asked questions

What does the Plan Release AI skill do?

RELEASE-INDEX BUILDER — Sequence elaborated epics into specs/release-plan.yaml with WSJF ordering and BCP baselines. NOT a planning-spine substitute: it does not scope work (scope-work) or write story tasks (plan-work). Use after elaborate-spec when the user wants a versioned release index of epics.

Why use Plan Release on TypingMind?

Because you install it once and use it with any model. Plan Release 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 Plan Release in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielvm-git/bigpowers/tree/main/skills/plan-release. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plan Release?

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 Plan Release?

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

Is the Plan Release AI skill free?

Yes. It is published on GitHub by danielvm-git 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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