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Speq Plan

Community
marconae
speq-plan

Plans a feature or change through a clarifying interview, producing spec deltas, plan.md, and decision-log.md with adversarial review. Use to plan, spec, design, or scope a feature, change, removal, refactor, or fix, before any implementation.

Overview

Publishermarconae
Repositoryspeq-skill
Skill namespeq-plan
Stars
54
Forks
10
Bundled files
7
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.

  • 7 bundled files

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

  • Open source

    Published by marconae on GitHub. Read the source before you install it.

Installation

Install the Speq Plan 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/marconae/speq-skill/tree/main/.claude/skills/speq-plan
  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/marconae/speq-skill.git /tmp/speq-skill
mkdir -p .claude/skills
cp -r /tmp/speq-skill/.claude/skills/speq-plan .claude/skills/speq-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speq Plan 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 Speq Plan 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 Speq Plan 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.

Spec Planner (Orchestrator)

This skill is a thin orchestrator. It runs the clarifying interview, collects context, and delegates the planning work (spec delta authoring, test mapping, task decomposition) to planner-agent.

Required Skills (for the orchestrator)

Invoke before starting:

  • /speq-cli: spec discovery

planner-agent and plan-reviewer invoke their own required skills.

Workflow

0. Load Project Hook (orchestrator)

Check for .speq/plan-hook.md in the repo root.

  • Present: read it. Announce "Loaded project hook: .speq/plan-hook.md". Its content is authoritative: it can add to, change, or override any part of this workflow. If the hook conflicts with this workflow, the hook wins.
  • Absent: continue without mention.

1. Discovery (orchestrator)

Run the speq CLI:

bash
speq domain list
speq feature list
grep -rhE -A3 '^## ADR:|^\*\*(ID|Status):\*\*' specs/_decision/ 2>/dev/null || echo none   # ADR digest, per /speq-cli

Name the features in the list that relate to the request, per /speq-cli's absence check.

Note whether specs/architecture.md exists. Pass its path, do not read it.

Collect only enough context for good interview questions, not a full exploration.

2. Clarifying Interview (orchestrator)

Apply the Socratic Method by asking the user. Never assume. Decompose the problem space with MECE partitioning:

  • Probe: surface hidden assumptions with open-ended questions
  • Partition: present alternative solutions as MECE options
  • Challenge: test design tradeoffs with counterexamples

Split check. If the work spans more than one independent phase, or more than about 4 features, propose one plan per phase. Plan the first phase now. Each later plan names the earlier plan in its Existing Context.

Keep each exchange as one line: - Q: <question> → A: <answer>, a few words each. This is the only copy of the interview: step 3 writes it into decision-log.md and the briefs point to it.

3. Plan Name (orchestrator)

Pattern: <verb>-<feature-scope>[-<qualifier>]

Once the name is set, write specs/_plans/<plan-name>/decision-log.md with the H1 # Decision Log: <plan-name> and an ## Interview section holding the step 2 lines. Do not paste the interview into any brief.

VerbWhen
addNew feature
changeModify existing
removeDeprecate/delete
refactorRestructure, same behavior
fixBug or spec mismatch

4. Delegate to planner-agent

Spawn the planner sub-agent with everything it needs:

Delegate to planner-agent — Plan <plan-name>

## Plan Name
<plan-name>

## User Intent
<1-3 sentence summary of what the user wants>

## Clarifying Interview Results
Read `## Interview` in `specs/_plans/<plan-name>/decision-log.md`. Keep it as is. Do not copy it into other files.

## Existing Context
<the exact `speq domain list` / `speq feature list` / `speq feature get` calls you ran, each followed by its output (the ADR digest, not the full `speq decision-log show`; name features you want read, do not paste `speq feature get` output); name `specs/architecture.md` by path when it exists, do not paste it>

## External Research
<any research already conducted, or "none — agent to research as needed">

## Project Hook
<if active: note ".speq/plan-hook.md — read it and apply it" — otherwise omit this section>

## Your Task
Produce spec deltas and plan.md per the `planner-agent` workflow. Tag tasks requiring deep reasoning with [expert] so the implementer orchestrator can route them to implementer-expert-agent.

If the interview agreed a split, end `plan.md` Context with a `Follow-up plans` list (name and one-line scope each).

Return the list of files created and the validation result.

5. Review planner-agent output (orchestrator)

When the sub-agent returns:

  1. Confirm speq plan validate <plan-name> passed. Re-run if uncertain.
  2. List all created files.
  3. If the sub-agent escalated a question, resolve it with the user and respawn with the clarification.

6. Adversarial Plan Review (orchestrator)

Spawn plan-reviewer once to challenge the plan before handoff. There is one review round.

Delegate to plan-reviewer — Review <plan-name>

## Plan Name
<plan-name>

## User Intent
<verbatim original request>

## Clarifying Interview Results
Read `## Interview` in `specs/_plans/<plan-name>/decision-log.md`.

## Plan Artifacts
plan.md, decision-log.md, every specs/_plans/<plan-name>/**/spec.md delta, and specs/_plans/<plan-name>/architecture.md when present

## Project Hook
<if active: note ".speq/plan-hook.md — read it and apply it" — otherwise omit this section>

It writes its findings to specs/_plans/<plan-name>/review/review.md and returns only PLAN REVIEW: BLOCKERS: <n>, FINDINGS: <n>, INTENT: <n> — <path>. INTENT counts the BLOCKERs on the Intent Fidelity axis alone.

If INTENT > 0: the reviewer holds that the plan solves a different problem than the one asked. Read the Intent-Fidelity findings from the review file. Ask the user about them before any fix pass. The user accepts the plan as-is, or gives guidance and you respawn planner-agent manually.

If INTENT == 0 and BLOCKERS > 0: respawn planner-agent once with the path to review/review.md. Instruct it to read the BLOCKER findings from that file, execute each Fix: line, log a [plan-review]-prefixed ## Review Findings entry in decision-log.md only for a blocker whose fix changed a design decision, re-run speq plan validate, and return its per-finding Resolved:/Could not resolve: report per /speq-planning's Revision Mode. Do not review again.

Any Could not resolve: line, or a failed speq plan validate: ask the user, one line of context each plus a pointer to the review file. The user accepts the risk and proceeds, or gives guidance and you respawn planner-agent manually.

Non-blocking findings never loop and are never persisted. Read them from the review file and carry them into step 7's report.

7. Explain next steps (orchestrator)

Print to the terminal:

  • plan.md's ## Impact section
  • Print ADR candidates: none, or list the title of each Promotes to ADR: yes entry in decision-log.md
  • Report non-blocking findings from step 6, read from the review file
  • If step 6 ran a fix pass, name it in one line ("N review findings fixed") and do not restate each one
  • Tell the user to run /speq-implement <plan-name> to continue
  • Tell the user to run /clear to implement with a fresh context window

Spec Hierarchy (reference)

specs/
├── <domain>/<feature>/spec.md     # Permanent
├── _plans/<plan-name>/            # Active
└── _recorded/<plan-name>/         # Archived

Work Split (reference)

StepPerformed by
Discovery, interview, coordinationThis skill
Spec delta authoring, plan context and decision log, task decompositionplanner-agent sub-agent
Adversarial review, revision loopplan-reviewer sub-agent

Anti-Patterns

PatternWhy Wrong
Skipping the clarifying interviewContent comes from user answers, never assumptions
Running a second review after the fix passOne review, one fix pass; unresolved findings go to the user
Persisting non-blocking findings or looping on themReport-only; they never gate

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 Speq Plan AI skill do?

Plans a feature or change through a clarifying interview, producing spec deltas, plan.md, and decision-log.md with adversarial review. Use to plan, spec, design, or scope a feature, change, removal, refactor, or fix, before any implementation.

Why use Speq Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/marconae/speq-skill/tree/main/.claude/skills/speq-plan. 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 Speq Plan?

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 Speq Plan?

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

Is the Speq Plan AI skill free?

Yes. It is published on GitHub by marconae 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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