Mission Specify

Organization

tikalk

mission-specify

Use when a mission brief must become a measurable spec — restates every success criterion as a binary outcome and resolves what cannot be measured before any plan exists.

Overview

Publisher	tikalk
Repository	`adlc-team-skills`
Skill name	`mission-specify`
Stars	144
Forks	2
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 tikalk on GitHub. Read the source before you install it.

Installation

Install the Mission Specify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/tikalk/adlc-team-skills/tree/main/skills/mission/mission-specify
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.

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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/mission/mission-specify .claude/skills/mission-specify

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mission Specify 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 Mission Specify 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 Mission Specify 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.

mission-specify

Requires: the factory-mission skill's shared executor contract at .agents/skills/factory-mission/references/executor.md (installed path); install with adlc-cli skills add tikalk/adlc-team-skills --skill factory-mission.

In a local session — you answer the gates

When ADLC_RUN_ID is not set, a person is in this session, and nothing else will answer a gate. The agentic-container runtime that relays gates to Slack exists only in a runner pod.

A paused run is your turn, not the end of the mission. When adlc-cli workflow run or resume exits with the run paused at a gate (run_paused, status: paused in adlc-cli workflow state show <run_id>), ask the person the gate's question and options. Then run adlc-cli workflow resume <run_id> --input <verdict_input>=<answer> and keep going. Never end your turn on a paused run that nobody was asked about.
Every gate that approves MRs is followed by a step that opens them. A gate whose message promises MRs/PRs is followed by a publish step: run-branch.sh and open-mr.sh for each repository the change touches, writing mr_url (mr_urls for more than one). Only then comes the write-back step, so the completion comment links real MRs. A gate with nothing after it to act on its answer is a plan defect; fix the plan before you start the run.
Before ending your turn, the run is completed, failed or aborted, or it is paused at a gate the person has been asked about.

What this skill does

mission-specify owns the specify phase of the mission SDD family: it turns the mission's brief.md into a spec.md whose acceptance criteria can each be judged yes or no, and it records the decisions taken to get there. It works under the shared executor contract (.agents/skills/factory-mission/references/executor.md): the specify step reads brief.md and emits a draft, and the clarify step reads spec.md and emits a decision, with reads_from resolved by the orchestrator. It stops at the spec — planning belongs to mission-plan, building to mission-implement, verification and convergence to mission-verify, and REVIEW and SHIP belong to factory-review and factory-deploy.

Default lane

Read brief.md; treat its goal, constraints, non-goals and success criteria as the input, not as wording to preserve.
Restate every success criterion as a binary, measurable outcome — each one answers a yes/no question through a command that exits, an artifact that exists, or a diff that shows the change.
Write spec.md: outcome, constraints, non-goals, acceptance criteria, and out of scope. Each acceptance criterion stays traceable to the success criterion it restates.
Surface any criterion that cannot be measured and any constraint that conflicts with another; do not paper over either in prose.
Resolve each surfaced item at the clarify phase — ask the person in a gated run, or in autonomous mode record the decision with its basis. The resulting decision is the clarify output.
Never select technologies and never plan files; those are later phases.

Step contract

Step	phase_type	output_type	reads_from	artifact
`specify`	generate	draft	`brief.md`	`spec.md`
`clarify`	clarify	decision	`spec.md`	—

Gates

No gate in this phase.

Boundaries

No worktree: isolation belongs to the orchestrator; this skill works in the tree it is handed.
No lease: never acquire, renew or release a run lease.
No tracker writes: no comments, labels, status transitions, or MR/PR creation.
No merge or approval: this skill never approves, merges or ships.
Routes back: an unmeasurable criterion or a conflicting constraint stays at clarify until resolved; a spec later proven wrong is corrected through the owning clarify phase, never by quietly editing acceptance criteria downstream.

Frequently asked questions

What does the Mission Specify AI skill do?

Use when a mission brief must become a measurable spec — restates every success criterion as a binary outcome and resolves what cannot be measured before any plan exists.

Why use Mission Specify on TypingMind?

Because you install it once and use it with any model. Mission Specify 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 Mission Specify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/mission/mission-specify. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mission Specify?

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 Mission Specify?

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

Is the Mission Specify AI skill free?

Yes. It is published on GitHub by tikalk 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

mission-specify

In a local session — you answer the gates

What this skill does

Default lane

Step contract

Gates

Boundaries

Frequently asked questions

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