Factory Mission logo

Factory Mission

Organization
tikalk
factory-mission

Use when executing a spec-gated mission from the queue — inner loop specify → plan → implement ↔ converge with tracker integration, worktree isolation, and a circuit breaker.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill namefactory-mission
Stars
144
Forks
2
Bundled files
17
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.

  • 17 bundled files

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

  • Open source

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

Installation

Install the Factory Mission 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/factory/factory-mission
  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/factory/factory-mission .claude/skills/factory-mission
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Factory Mission 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 Factory Mission 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 Factory Mission 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.

factory-mission

In a factory run — read this first

When ADLC_RUN_ID is set, the workflow engine runs this skill unattended as its execute step, and the completion gate follows it. Do exactly this:

  1. The mission is the run's issue. Take it from --issue <ref> in the arguments; when that is missing or empty, use ADLC_ISSUE_REF. With neither, do not ask: write {"conclusion":"blocked: factory run without an issue"} into ADLC_STEP_OUTPUT and return.
  2. Run the mission for that issue end to end (Phases 0–6 below): brief, test (RED), code (GREEN), converge, then open or reuse the draft code MR/PR in the repository the change belongs to.
  3. Before you return, always: write mr_url (the code MR/PR you opened or reused) and a one-line conclusion into ADLC_STEP_OUTPUT — one flat JSON object of string values; keep any keys already in the file. The engine links mr_url at the completion gate. Do this whatever the outcome; with no MR, write only the conclusion.
  4. Never ask a question or wait for an answer. The completion gate after this step is the approval; a question here ends the step with nothing implemented.

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

factory-mission is the execution-harness orchestrator of the software factory. It takes a feature description, structures it into a Mission Brief (goal, constraints, non-goals, success criteria), generates an ordered step list, and executes those steps via sequential subagent runs.

It implements the following key factory platform capabilities:

  1. Universal Skill Routing: Decoupled step dispatching. It scans installed skills and hands the inventory to subagents (the model picks which tool fits the step).
  2. Tracker-Agnostic Integration (references/tracker-integration.md): When invoked with --issue <ref>, it pulls ticket context, respects automation/dispatch labels (autonomous/supervised), and writes back status comments + iteration logs.
  3. Inter-Agent Comment Bus (references/tracker-integration.md §Inter-Agent Comment Bus): When tracker-integrated, each step's terminal output (decisions, findings, artifact references — never drafts) is published as a structured comment on the PR/MR/issue. The next step reads previous markers before starting. This is the durable inter-agent memory that survives session boundaries, runtime switches, and pod crashes. Drafts stay on local disk.
  4. Self-Contained Worker Brief: The Mission Brief is persisted to .adlc/workflows/runs/<run_id>/brief.md as a draft. Resumed runs and cross-runtime workers read it from disk — no session-context dependency.
  5. Worktree Isolation: Each run gets its own git worktree. Never touches the user's main checkout. Cleaned up on exit (retained if unsaved work).
  6. Lease-Based Liveness: The state file carries a renewable lease with heartbeat + TTL. Resume can distinguish live, stale, and completed runs.
  7. Stall Detection: After dispatching a subagent, observable progress is checked at a configurable window (default 20 min). A hung agent that passes the circuit breaker is detected and killed.
  8. Lane-Based Dispatch (references/lanes.md): Steps can run on different lanes — inline (this session), agent (fresh session of same CLI for maker/checker separation), or cli:<runtime> (optional cross-vendor). The agent lane is the default for unattended stages.
  9. Scratchpad Tools: Subagents share named, run-private scratchpads (.adlc/workflows/runs/<run_id>/scratchpads/<name>.txt) to compile notes, drafts, and reviews incrementally before publishing.
  10. Workflow Memory & Self-Improvement: Persistent JSONL database (.adlc/workflows/memory.jsonl, workspace-global) stores learnings across runs. factory-learn periodically runs retrospectives to prune/weight memories.
  11. Hierarchical Context Parameters: Workflows and agents reference parameters as {{params.<key>}}, resolved from most specific to least specific: agent < workflow < repository < project < default.
  12. Decoupled Test/Code Separation: Test-first build with mechanical gates lives in mission-implement; converging judgement in mission-verify.

When to use

  • "Build this feature end to end" inside a factory-enabled team.
  • You want an execution loop with a circuit breaker, score-regression checking, and a robust resume mechanism (factory-mission --resume).
  • You want to run autonomously against a ticket queue.

When NOT to use:

  • For non-factory standalone projects (run adlc-cli workflow run <workflow.yml> — the CLI engine, shared run state).
  • Trivial 1-line changes (do them directly).

Process

factory-mission executes in alignment with the shared executor contract (references/executor.md) and the tracker-agnostic layer (references/tracker-integration.md).

Phase 0 to 4: Setup & Compilation

  1. Read the run's mission.yml (.adlc/workflows/runs/<run_id>/mission.yml). Resolve execution and supervision.
  2. If --issue <ref> is specified (in a factory run, ADLC_ISSUE_REF when it is not):
    • Discover credentials and MCP/CLI tools (references/tracker-integration.md).
    • Pull the issue content as the primary Brief description.
    • Read the labels. If dispatch is interactive or gating is human-required -> HALT execution (hand back to interactive session).
    • Dependency hard gate (Operation 12): read the issue's **Depends on** line, resolve each dependency's state via the connector, and refuse when any dependency is still open (only closed / cancelled count complete — spec-gated does NOT satisfy the gate). On refusal: publish a blocked: dependency-gate comment naming the blocking issue(s), set the issue's lifecycle label back to spec-gated, release the lease (ADLC_STEP_OUTPUT conclusion blocked: dependency-gate on <refs>), and return without touching worktree, code, or executor. When the tracker exposes native blocked_by/blocking relations (GitLab issue links, Linear/Jira relations), use them in addition to the brief's Depends-on line — a Blocked/blocked-by signal from the tracker also triggers the gate.
    • The comment bus is active for this run — step outputs will be published as marker comments on the PR/MR/issue.
  3. If no issue (outside a factory run): read spec description from arguments. The comment bus is inactive — steps communicate through local files only.
  4. Structure the Mission Brief (Goal, Constraints, Non-Goals, Success Criteria). The Brief is a draft — not published to the comment bus.
  5. Resolve the framework lane (framework: in mission.yml → detect-framework.sh / detect-framework.ps1, frozen precedence: override → git-dated usage evidence → installed packs → embedded default) and disclose the resolved lane plus its evidence in the run brief (**Framework lane:** <lane> (<evidence>)).
  6. Resolve hierarchical Context Parameters from agent < workflow < repository < project < default and embed the frozen value map in the brief.
  7. Generate the step list based on route classification (spec, change, quick). Each step declares output_type (draft/decision/findings/artifact-ref) and reads_from (markers or local paths) per the executor contract.

Team index fallback: when no team record-class index was injected at session start, read the binding records directly from docs/adlc/memory/ (dual-read order: docs/adlc/memory first, legacy .adlc/memory fallback) and state that fallback in one line. Never block on the missing injection.

Lifecycle DAG & Step Resolution

factory-mission resolves each lifecycle step to the mission-* skill that owns it. Step order, inputs, and outputs follow the table; the owning skills define the discipline.

StepSkilloutput_typereads_from
specifymission-specifydraftbrief.md
clarifymission-specifydecisionspec.md
planmission-plandraftspec.md, clarify
testmission-implementartifact-refspec.md
codemission-implementartifact-reftest, spec.md, plan.md
verifymission-verifyfindingstest, code
convergemission-verifydecisionbrief.md

A SPEC_CORRECTION_NEEDED verdict routes to the owning clarify gate (mission-specify), never to code.

Boundary

factory-mission owns the execution harness: the worktree, the lease, tracker integration (issue intake, the comment bus, completion write-back), and the draft code MR. It never owns spec content, tests, code, or verdicts — each step's output belongs to the mission-* skill that produced it. Dispatch names phases, not commands: every lifecycle step resolves through the table above.

Shared Executor Overrides

Knobs specific to a mission run, read from mission.yml (the policy home for execution and supervision):

  1. Supervision: the run's supervision mode — unattended runs never ask a question.
  2. Quality threshold: quality_threshold travels in as a converge input owned by mission-verify; the orchestrator carries it, never applies it.
  3. Test/code split: selected per run — whether the build dispatches separate test and code steps.

Phase 6: Completion & Write-Back

  1. If tracker-integrated: read all marker comments from the PR/MR/issue to compile the audit trail (converge decisions, test findings, implement artifact references, convergence history).
  2. Close the shared run: final adlc-cli workflow state advance <run_id> --step <last> --status completed (the run dir is the durable archive; no separate move).
  3. Write per-implement logs to iterations.md.
  4. If tracker-integrated:
    • Post completion summary as a ticket comment (marker: factory-mission:status=completed:run=<run_id>).
    • Transition lifecycle label from executing to validation (or done if merged).
    • Stamp agent-authored on opened PRs.
    • In a factory run, write the code MR/PR URL as mr_url into ADLC_STEP_OUTPUT (see "In a factory run" above).
    • Defer PR merge to code-owner approval (never auto-merge without approval).
  5. Output the complete audit summary.

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 Factory Mission AI skill do?

Use when executing a spec-gated mission from the queue — inner loop specify → plan → implement ↔ converge with tracker integration, worktree isolation, and a circuit breaker.

Why use Factory Mission on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/factory/factory-mission. 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 Factory Mission?

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 Factory Mission?

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

Is the Factory Mission 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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