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Boringoperator Wo

Community
boringmarketer
boringoperator-wo

Author a work order for a boringoperator lane (terra build or sol review) with proper context loading, per James's professional-process directive

Overview

Publisherboringmarketer
Repositorykimi-first
Skill nameboringoperator-wo
Stars
51
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 boringmarketer on GitHub. Read the source before you install it.

Installation

Install the Boringoperator Wo 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/boringmarketer/kimi-first/tree/main/skills/boringoperator-wo
  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/boringmarketer/kimi-first.git /tmp/kimi-first
mkdir -p .claude/skills
cp -r /tmp/kimi-first/skills/boringoperator-wo .claude/skills/boringoperator-wo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Boringoperator Wo 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 Boringoperator Wo 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 Boringoperator Wo 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.

boringoperator WO authoring

Every codex WO for ~/Code/boringoperator follows this contract:

  1. FIRST LINE of every prompt: "Read dev-docs/wo-context-pack.md at the repo root FIRST — it is binding context (mission, laws, dialect, loop, hygiene)." Never re-paste pack content into prompts; reference it. Add ONLY:

    • the lane's goal in one paragraph
    • verified defects/evidence (file:line, DB audit results) — never vibes
    • lane-specific constraints (files owned by other unmerged waves, migration number claimed after checking main AND open branches, gates that stay off)
    • the proof contract and output shape
  2. Builder lanes: terra ultra, fresh worktree, npm ci first. Reviewer lanes: sol, high for product surfaces, xhigh when migrations/custody in scope; sol writes REVIEW-.md and is the only one who touches it.

  3. Fix rounds: reference the review file by name, quote nothing that the review already states — "read Round N in full" + clear-condition list.

  4. After every lane lands: Claude live-films, records the design verdict, then dispatches sol. Merge order: one train at a time; capability-map conflicts resolve by regeneration on the merged tree; hosted migrations apply BEFORE app deploys that read them.

  5. SIMPLICITY COUNTERWEIGHT (James, Sep 1): codex tends to overengineer. Every builder WO carries the context pack's "Solution style" section as binding; when reviewing terra's output, treat unrequested abstraction, knobs, layers, or breadth as findings to strip, not bonuses. Ask of every diff: is there a smaller true change?

  6. WO IS A FILE (Sep 1): every WO lives at dev-docs/wo-.md and is committed to main BEFORE dispatch; "ready to dispatch" in memory alone is not ready (the readback-truth WO had to be re-derived from scratch). Evidence rows the WO cites go to dev-docs/evidence/-.json.

  7. EVIDENCE-FIRST DIAGNOSIS (Sep 1): before a root-cause lane, Claude pulls the hosted rows read-only (Management API database/query as postgres, wrapped in begin; set transaction read only; …; commit;; token = keychain "Supabase CLI" go-keyring-base64 decoded) and states in the WO what is VERIFIED, what is NOT a defect, and what is NOT evidence. Two of three checkpointed hypotheses were wrong on the readback lane; a codex lane dispatched on them would have chased phantoms. When live provider reads are needed, D0 of the lane = a read-only diagnostic script Claude runs with creds — codex never sees creds.

  8. MERGE TRAIN READS THE WORKTREE, NOT MEMORY: migration list, conflict dry-run (git merge-tree --write-tree --name-only main <branch>), and the recovery drill on the MERGED chain (main's newer migrations + the branch's have never been applied together). Design verdict gates the merge alongside sol's; the merge itself is mechanical → Sonnet seat, Fable reviews the merge diff and pushes.

  9. Zoom-out check before dispatching any lane: does this serve the mission (finished work, one gesture, Main Street)? Is it dollar-ranked? Is it class-1 (ceremony) or product-speed (no ceremony)? Would James call it overengineering?

  10. LESSONS COMPOUND IN THE PACK (James, Sep 2): after every certification, distill any NEW blocker class into dev-docs/wo-context-pack.md "Standing lessons" (binding; builders report against it, reviewers attack it first). A lesson that lives only in Claude's memory never reaches a lane. Keep the list short; retire items that stop recurring.

  11. READ RESULTS BEFORE STAMPING: assert gh pr view --json state == MERGED before any "merged" record line or worktree removal; stamps from date -u; one proof run per worktree at a time; never codex exec resume --last with parallel lanes.

  12. LOOP V3 (James, Sep 3 — binding; see ship-lane skill and repo dev-docs/loop-v3.md): classify the lane A/B/C first and never mix; ONE review pass with a checklist (sol once for A, design once on frames for B, none for C), ONE fix, merge; residue → the weekly punch batch; activation is supervised-first; ship through scripts/train.sh → verify--live.sh → scripts/activate-flag.sh; done = live on DD with a receipt. Every WO uses dev-docs/wo-template.md. The "after every lane lands: film → design → sol" sentence in item 4 is superseded by this item.

  13. SEAT BRIEFS (Sep 4): every builder/reviewer brief says "no watcher or monitor will wake you; poll yourself (sleep 30, bounded to 40 min)". Three seats stalled on armed watchers in one night.

  14. DESIGN VERDICTS (Sep 4): the design seat writes into the main checkout; the driver commits the verdict file immediately ([skip ci]) — an untracked file there makes the deploy guards refuse (dirty tree) and cost a redeploy tonight.

Frequently asked questions

What does the Boringoperator Wo AI skill do?

Author a work order for a boringoperator lane (terra build or sol review) with proper context loading, per James's professional-process directive

Why use Boringoperator Wo on TypingMind?

Because you install it once and use it with any model. Boringoperator Wo 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 Boringoperator Wo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boringmarketer/kimi-first/tree/main/skills/boringoperator-wo. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Boringoperator Wo?

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 Boringoperator Wo?

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

Is the Boringoperator Wo AI skill free?

Yes. It is published on GitHub by boringmarketer 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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