Intent To Mission Blueprint logo

Intent To Mission Blueprint

Organization
frumu-ai
intent-to-mission-blueprint

Convert human operational intent into a Tandem mission blueprint with phased workstreams, explicit handoffs, reusable outputs, and recurrence-aware setup.

Overview

Publisherfrumu-ai
Repositorytandem
Skill nameintent-to-mission-blueprint
Stars
121
Forks
13
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 frumu-ai on GitHub. Read the source before you install it.

Installation

Install the Intent To Mission Blueprint 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/frumu-ai/tandem.git /tmp/tandem
mkdir -p .claude/skills
cp -r /tmp/tandem/apps/tandem-desktop/src-tauri/resources/skill-templates/intent-to-mission-blueprint .claude/skills/intent-to-mission-blueprint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intent To Mission Blueprint 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 Intent To Mission Blueprint 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 Intent To Mission Blueprint 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.

Intent To Mission Blueprint

Purpose

Use this skill when a human describes an ongoing operational goal and you need to turn that intent into a Tandem MissionBlueprint.

This skill is specifically for translating human intent into:

  • a mission goal
  • success criteria
  • shared context
  • workstreams
  • dependencies
  • output contracts
  • review or approval gates
  • recurrence-aware setup

It is not for executing the mission itself.

What Good Output Looks Like

Return one mission blueprint only.

The blueprint should be:

  • staged
  • scoped
  • operationally understandable
  • safe for long-running reuse
  • concrete enough to compile without hand-waving

The result should feel like something an operator could schedule and trust, not a brainstorming outline.

Core Translation Rule

Translate the user’s intent into the smallest coherent staged mission.

Do not:

  • explode one intent into ten weak stages
  • collapse everything into one vague workstream
  • create review gates everywhere
  • treat raw intermediate output as reusable truth

How To Interpret Human Intent

1. Find the real mission outcome

Convert vague requests into one concrete shared outcome.

Example:

  • Human intent: "keep the team updated on important operational changes every morning"
  • Mission goal: "Produce a reviewed daily operations update with validated changes, recommended actions, and an operator-ready handoff."

2. Find the repeated operating loop

Most long-running missions follow one of a few durable shapes:

  • monitor -> analyze -> decide -> handoff
  • intake -> plan -> execute -> verify -> review
  • collect -> consolidate -> update state -> notify

Choose the smallest loop that matches the intent.

3. Identify the real handoffs

Each downstream stage should consume a clear upstream output.

Use depends_on and input_refs only where the handoff is real.

4. Design for recurrence

If the mission will run daily, weekly, or continuously:

  • prefer stable artifacts
  • make later stages reuse validated upstream work
  • avoid forcing broad rediscovery every run
  • include review or approval only where trust actually matters

5. Keep promoted outputs distinct from raw working state

Do not design the mission so every stage treats draft notes as durable truth.

Instead:

  • discovery stages produce working artifacts
  • synthesis or execution stages produce decision-ready artifacts
  • review or approval stages determine what is ready for downstream reuse

Mission Authoring Rules

Goal

The mission goal should describe the end state, not the implementation steps.

Success criteria

Use measurable or inspectable checks.

Good:

  • "Final handoff identifies validated changes, required decisions, and clear next actions."

Weak:

  • "Mission is helpful and thorough."

Shared context

Include only stable mission-wide constraints:

  • audience
  • deadlines
  • allowed sources
  • risk constraints
  • compliance or review rules
  • quality bar
  • cadence assumptions

Workstreams

Each workstream must have:

  • one responsibility
  • one main artifact or result
  • a clear prompt
  • bounded scope

Review stages

Add review, test, or approval stages only when they:

  • protect downstream trust
  • control promotion or delivery
  • validate external action readiness
  • catch failures that earlier stages are likely to miss

Long-Running Mission Guidance

When the intent implies recurring operation over weeks or months:

  • assume project-scoped knowledge reuse
  • keep trust floor at promoted by default
  • design stages so later runs can build on validated outputs
  • avoid requiring every run to rediscover unchanged context
  • keep stages inspectable and repairable

Default Staged Patterns

Pattern 1: Monitor -> Analyze -> Decide -> Handoff

Use for:

  • operational monitoring
  • workflow surveillance
  • recurring change detection
  • market or environment tracking

Pattern 2: Intake -> Plan -> Execute -> Verify -> Review

Use for:

  • execution pipelines
  • implementation work
  • structured project delivery
  • repeated operational processing

Pattern 3: Collect -> Consolidate -> Update State -> Notify

Use for:

  • recurring state refresh
  • knowledge or system updates
  • periodic status synthesis
  • publish/update loops

Prompt-Writing Standard For Workstreams

Every workstream prompt should include:

  • role
  • local mission
  • allowed inputs
  • output contract
  • guardrails

Good prompt skeleton:

text
Act as the [role]. Use the named upstream inputs and approved workspace context only. Produce the required output in a form the next stage can consume directly. Preserve relevant evidence, avoid redoing upstream work, and record uncertainty or blockers explicitly.

Output Rules

Return valid YAML only.

Include:

  • id
  • label
  • description
  • schedule_defaults
  • blueprint

The blueprint must contain:

  • mission_id
  • title
  • goal
  • success_criteria
  • shared_context
  • workspace_root
  • phases
  • milestones
  • team
  • workstreams
  • review_stages

Strong Final Meta-Prompt

Use this when asked to generate a Tandem mission blueprint from human intent:

text
Convert the following human operational intent into one Tandem mission blueprint.

Requirements:
- Return YAML only.
- Produce one shared mission goal.
- Use the smallest coherent staged mission shape.
- Give each workstream one clear responsibility.
- Use explicit dependencies and input refs only for real handoffs.
- Every workstream must have a concrete prompt and output contract.
- Design for recurring execution when the intent implies daily, weekly, or long-running operation.
- Default to project-scoped promoted knowledge reuse through validated outputs.
- Add review or approval gates only where they materially improve trust, verification, or promotion control.
- Avoid vague stages, vague prompts, and vague outputs.

Human intent:
[insert intent]

Constraints:
[insert constraints]

Preferred workspace root:
[insert workspace root]

Preferred cadence:
[insert cadence]

Frequently asked questions

What does the Intent To Mission Blueprint AI skill do?

Convert human operational intent into a Tandem mission blueprint with phased workstreams, explicit handoffs, reusable outputs, and recurrence-aware setup.

Why use Intent To Mission Blueprint on TypingMind?

Because you install it once and use it with any model. Intent To Mission Blueprint 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 Intent To Mission Blueprint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/frumu-ai/tandem/tree/main/apps/tandem-desktop/src-tauri/resources/skill-templates/intent-to-mission-blueprint. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Intent To Mission Blueprint?

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 Intent To Mission Blueprint?

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

Is the Intent To Mission Blueprint AI skill free?

It is published on GitHub by frumu-ai. 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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