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Mission Slice Sop

CommunityPopular
mvschwarz
mission-slice-sop

Use when starting, building, handing off, restoring, or closing a mission or slice under the default lightweight SDLC.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namemission-slice-sop
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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 mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Mission Slice Sop 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/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/mission-slice-sop
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/assets/plugins/openrig-core/skills/mission-slice-sop .claude/skills/mission-slice-sop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mission Slice Sop 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 Slice Sop 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 Slice Sop 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/Slice SOP — Part A

Use this skill to actually do mission/slice work: track on the canonical files, record what you proved, hand off through them, and survive compaction on them. Do the work described here; do not merely explain the protocol.

Full convention SSOT: repo docs/reference/sdlc-conventions.md; installed $OPENRIG_HOME/reference/sdlc-conventions.md. This skill is the concise Part-A teaching surface and does not replace the full convention.

Resolve the work first

Run rig whoami --json, then resolve project.yaml -> mission.yaml -> active slice.yaml -> selected component or wave map -> addressed context. The complete lookup and precedence rule is docs/reference/product-journey-sdlc.md#resolve-the-selected-path (installed: $OPENRIG_HOME/reference/product-journey-sdlc.md#resolve-the-selected-path). Read the selected addresses and source needed for this task; skills available in your profile are capabilities, not a mandatory reading list. No composition means light Part A. Role names and idle seats add no gates. Explicit rigor and authored wave boundaries retain their named checks.

The light inner loop is ground yourself → build → test with your own eyes → iterate → record and return the whole outcome. The builder may hold this check on a tiny change. Independent review runs only when selected, and once at an authored wave boundary rather than once per slice.

Part B is defined in the conventions SSOT and runs only when explicitly assigned to named work. If you believe it is needed, state the reason to the owner and continue on the selected path until that choice changes.

Proportionality — this SOP serves shipping; it is not the work itself

The working product is the deliverable. This bookkeeping exists so the work survives handoff, compaction, and review — nothing more. Match it to stakes. The rig scope audit backstop is advisory and fail-open: it never blocks a build and is not a gate you clear before proceeding. If you're spending more time on the convention files or the audit than on the running product, stop and go build. Running the full apparatus on a small change is the letter-worship failure, not diligence.

The three role contracts

  • Planning agent: records what the slice is for and what "done" looks like, in the slice's own words; produces mockups for UI deliverables so the builder has something to look at (a UI slice with no mockup is an incomplete plan; non-UI slices have none — not a gate).
  • Build agent: builds it, and looks at the mockups. Verifies by running the thing.
  • QA agent (owns the compare): compares planned vs delivered per deliverable, records the verdict and the note. On mismatch: fix-and-re-review, or kick back with the reason — never escalate a raw mismatch to the human.

These three are a division of labour, not a chain of gates. One agent may hold all three on a small slice.

The current file contract

These files are the operating surface of the work — you track on them, record on them, hand off through them, and survive compaction on them. Keep them current because that is what lets the work survive. But they serve the product; they are not the product. If you're polishing files while the actual thing isn't shipping, you've inverted it: go build, then update them.

  • Slice: SPEC.md, PROGRESS.md, PROOF.md, and proof/.
  • Mission: SPEC.md and NOTES.md.

Per-file rules — WHO / WHEN / HOW

SPEC.md

  • WHO: the planning owner authors it; builders and reviewers read it as the scope and intent source.
  • WHEN: at creation and whenever scope changes.
  • HOW: a slice carries intent: in frontmatter and opens with ## Intent, ## Mini-requirements, and ## Proof contract. A mission carries its intent and mission-level specification in the same authored node file. Keep depth proportional to the work.

PROGRESS.md

  • WHO: the orchestrator owns the current state; every agent logs its own outcomes.
  • WHEN: on material delivery-state changes and at acceptance.
  • HOW: keep current delivery state in ## Current state; append material outcomes with links to proof under ## Outcomes. Retain historical entries and existing checkboxes without treating them as current acceptance authority. For a selected proof policy, use rig proof show <slice> for current readiness; keep frontmatter stage/verified honest.

PROOF.md + proof/

  • WHO: the impl/QA pair that worked the slice.
  • WHEN: before you call a slice done.
  • HOW: say what you verified and how you verified it — by effect: you ran it and looked at the result. Put supporting media under proof/. State plainly what is proven and what is not; an honest "this half is untested" is worth more than a checkmark. If a drop verb is in play for this slice, prefer it over hand-placing files so the artifact carries its own provenance.

NOTES.md

  • WHO: any agent updates §1 (top-of-mind); each seat owns and appends to its own §A–§X.
  • WHEN: on material mission-context changes; a compacting agent files its state here BEFORE compaction and reads it on restore.
  • HOW: accruing tribal knowledge — §1 ≤ 5–15 lines (gates, open decisions, surprises); per-seat continuation entries (latest = truth; other seats read-only). Pointer-first; don't duplicate.

The lifecycle (4 legs)

SCAFFOLD (rig scope creates the files from templates) → POPULATE (agents fill them as work happens) → PROJECT (the Living Notes UI reads them into INTENT → PLAN → DELIVERED) → VERIFY (rig scope audit, advisory). "Loose freeform write + deterministic verify."

Hot-potato (handoffs)

End every turn by passing the ball — a rig queue handoff to the next agent. The handoff verb is transactional: it closes the source as handed-off and mints the successor owned by --to, so the baton cannot be dropped. A handoff terminates only at the orchestrator seat, which holds the context to judge whether a park is legitimate.

A plain rig queue create row is informational — a durable message. It is not a baton and does not carry this obligation. Use handoff when you are passing real work; use create when you are informing.

Never go idle holding a baton.

Verify (deterministic backstop)

Run rig scope audit at slice-close. Every convention check is advisory / fail-open — it records and advises, never blocks a write. Fix what's real, skip what isn't, keep moving. A clean audit score is not required to proceed.

Reading terminal captures — KNOWN GOTCHA: ghost-text autocomplete is NOT real

When you rig capture a pane, greyed / ghost autocomplete suggestions are NOT real content — they are autocomplete previews, not typed, staged, or committed input. This has been faking agents out a lot: reading a ghost suggestion in a peer's input box as "staged text they're about to send," then reasoning on a string that was never there.

Rule: ignore ghost/autosuggest text entirely. Only committed/rendered pane output is real. When it matters, verify at source (git, the queue, the actual event) — never off a capture's ghost line.

Moment-of-truth checklist

  • Starting a slice? → do you know what it's for and what done looks like? mockups attached (UI slices)? Are you on the light path? (You are, unless the mission owner assigned the overlay.)
  • Finishing a slice? → does PROOF.md say what you actually verified, by effect, including what is NOT covered? PROGRESS.md updated? mission NOTES.md refreshed? handed off via rig queue handoff?
  • Compacting? → file your state in mission NOTES.md; on restore, read it plus the active slice's SPEC.md, PROGRESS.md, and PROOF.md.
  • Starting on a mission? → read mission SPEC.md, NOTES.md, and the conventions SSOT.

Frequently asked questions

What does the Mission Slice Sop AI skill do?

Use when starting, building, handing off, restoring, or closing a mission or slice under the default lightweight SDLC.

Why use Mission Slice Sop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/mission-slice-sop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mission Slice Sop?

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 Slice Sop?

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

Is the Mission Slice Sop AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 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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