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Run Planning

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danielvm-git
run-planning

DISCOVER-PHASE ADVANCER — Drive the discover-phase checklist (specs/planning-status.yaml) through survey-context → scope-work → research-first → elaborate-spec → plan-release → slice-tasks. NOT a duplicate of plan-work or the planning spine; it orchestrates the pre-coding discover phase only.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namerun-planning
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 Run Planning 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/run-planning .claude/skills/run-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run Planning 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 Run Planning 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 Run Planning 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.

story: e24s03

Run Planning

HARD GATE — Before running planning skills, confirm the epic capsule exists and the active story is clear. Planning without a target is noise.

Role: DISCOVER-PHASE ADVANCER — orchestrates the discover-phase sequence; hands off to the scope-work → slice-tasks → plan-work spine for implementation planning.

Updates specs/planning-status.yaml as discover-phase skills complete. This is NOT a duplicate of plan-work — it orchestrates the pre-coding discovery phase only (Discover phase in the 6-phase PMBOK lifecycle), handing off to the planning spine for implementation detail.

When to use

  • Starting a brand-new feature or initiative with no prior planning artifacts
  • Returning to a stalled initiative and needing to resume the discovery workflow
  • After orchestrate-project hands off to the Discover phase
  • When a new epic emerges from change-request and needs to go through full discovery

Pre-flight

  • Does specs/planning-status.yaml exist? If not, create it with the default workflow keys.
  • Does specs/state.yaml have active_flow: planning? Set it if not already.
  • Is the epic identified in release-plan.yaml? The epic must exist before discovery begins.

Workflows (default keys)

  • survey-contextscope-workresearch-firstelaborate-spec (optional) → plan-releaseslice-tasks

Each key maps to a skill invocation. Optional keys can be skipped; required keys must complete before the phase advances.

Process

  1. Read state — Read specs/planning-status.yaml and specs/state.yaml. Understand where discovery stands: which workflow keys are done, which are pending, and which are optional (can be skipped).

  2. Find next step — Find the first workflow key with status: pending. If the key is optional, check if the user wants to run it. If not, mark it skipped.

2a. Context capsule check — Before invoking elaborate-spec, check whether a fresh specs/planning-context.yaml exists:

bash
test -f specs/planning-context.yaml && python3 -c "
import yaml, datetime
d = yaml.safe_load(open('specs/planning-context.yaml'))
written = d.get('written_at','')
if written:
 age = (datetime.datetime.now(datetime.timezone.utc) - datetime.datetime.fromisoformat(written)).total_seconds() / 3600
 print(f'Context age: {age:.1f}h')
" 2>/dev/null || echo "No context or no written_at"
  • If context is < 24h old, ask: "Planning context from Xh ago exists for '<feature_name>'. Re-run elaborate-spec? [y/N]". Skip elaborate-spec on N.
  • If context is ≥ 24h old or absent, run elaborate-spec normally.
  • On planning cycle completion (all required keys done), clear the capsule: delete specs/planning-context.yaml and set planning-status.yaml context_capsule: null.
  1. Invoke the matching skill — Run the skill that matches the workflow key:

    • survey-context — where are we?
    • scope-work — what's in and out?
    • research-first — what already exists?
    • elaborate-spec — refine the idea (optional)
    • plan-release — sequence epics by WSJF
    • slice-tasks — cut vertical slices
  2. Update status — On successful completion, set status: done for that workflow key in planning-status.yaml.

  3. Advance — Set state.yaml active_flow: planning while in this chain. When all required keys are done, set handoff.next_skill to plan-work.

Workflow Keys Schema

In specs/planning-status.yaml:

yaml
context_capsule:             # written by elaborate-spec; cleared on cycle completion
  written_at: "2026-06-22T03:00:00Z"
  written_by: elaborate-spec
  feature_name: "add dark mode"
workflows:
  survey-context:
    required: true
    status: done
  scope-work:
    required: true
    status: pending
  research-first:
    required: false
    status: optional
    note: "Skip if no external dependencies"
  elaborate-spec:
    required: false
    status: optional
  plan-release:
    required: true
    status: pending
  slice-tasks:
    required: true
    status: pending

Verify

→ verify: test -f specs/planning-status.yaml && [ "$(grep -c 'status: done' specs/planning-status.yaml)" -ge 3 ]

Frequently asked questions

What does the Run Planning AI skill do?

DISCOVER-PHASE ADVANCER — Drive the discover-phase checklist (specs/planning-status.yaml) through survey-context → scope-work → research-first → elaborate-spec → plan-release → slice-tasks. NOT a duplicate of plan-work or the planning spine; it orchestrates the pre-coding discover phase only.

Why use Run Planning on TypingMind?

Because you install it once and use it with any model. Run Planning 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 Run Planning in TypingMind?

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

Which AI models can use Run Planning?

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 Run Planning?

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

Is the Run Planning 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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