Mission Implement logo

Mission Implement

Organization
tikalk
mission-implement

Use when a plan must become working code under a mechanical test-first contract — a failing suite first, the minimum implementation second, and both closed by gates.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill namemission-implement
Stars
144
Forks
2
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 tikalk on GitHub. Read the source before you install it.

Installation

Install the Mission Implement 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/tikalk/adlc-team-skills/tree/main/skills/mission/mission-implement
  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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/mission/mission-implement .claude/skills/mission-implement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mission Implement 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 Implement 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 Implement 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-implement

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.

  1. 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.
  2. 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.
  3. 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-implement owns the build phase of the mission SDD family: it splits implementation into a test step that writes the failing suite and a code step that writes the minimum source to satisfy it, each closed by a mechanical gate. It works under the shared executor contract (.agents/skills/factory-mission/references/executor.md): the test step reads spec.md and emits an artifact-ref; the code step reads that test artifact plus spec.md and plan.md. It stops at a green suite — verification and convergence belong to mission-verify, and REVIEW and SHIP belong to factory-review and factory-deploy.

Default lane

  1. Write the failing suite first, based on spec.md, with tests/ as the only writable surface and source mounted read-only.
  2. Run it — the RED gate requires that at least one test fails before any implementation exists. A suite that passes immediately means the behavior exists or the assertions are empty: stop and route to the owning clarify gate for spec correction. Record the failing count in the run log.
  3. Write the minimum code with source as the only writable surface and tests/, spec.md and plan.md mounted read-only.
  4. Run the suite — the GREEN gate requires the full suite passes before converge. A failure loops back to the code step with the failure list attached; the loop is bounded by the mission's code-step retry cap and does not consume the converge circuit breaker.
  5. Skip the split only when the run declares no test surface: tdd: false, docs/config-only work, or interactive mode. A code-bearing step in autonomous or supervised mode never skips it.

Step contract

Stepphase_typeoutput_typereads_fromartifact
testbuildartifact-refspec.mdtests/
codebuildartifact-reftest, spec.md, plan.mdsrc/

Gates

Both gates are mechanical and bind the two build steps of this skill.

  • RED gate — after test. The suite is run before any implementation exists, with source mounted read-only. RED passes only when at least one test fails, and the failing count is recorded in the run log. A suite that passes immediately means the behavior already exists or the assertions are empty: the gate fails and the run routes to the owning clarify gate for spec correction instead of advancing to code.
  • GREEN gate — after code. The suite is run again with tests/, spec.md and plan.md mounted read-only. GREEN passes only when the full suite passes. Any failure returns the failure list to the code step, bounded by the mission's code-step retry cap, and that loop-back does not consume the converge circuit breaker — only broken iterations do.
  • converge is never reached with a red suite: GREEN is the last mechanical check before verification, and a red run stops at the gate.
  • The split is skipped only when the run declares no test surface: tdd: false, docs/config-only work, or interactive mode.

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: a suite that passes before implementation, or a spec or plan that cannot be implemented as written, routes to the owning clarify gate for spec correction; a GREEN failure loops back to code with the failure list attached, bounded by the mission's code-step retry cap, and never weakens the tests to pass.

Frequently asked questions

What does the Mission Implement AI skill do?

Use when a plan must become working code under a mechanical test-first contract — a failing suite first, the minimum implementation second, and both closed by gates.

Why use Mission Implement on TypingMind?

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

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

Which AI models can use Mission Implement?

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 Implement?

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

Is the Mission Implement 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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