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Octocode Subagent

Community
bgauryy
octocode-subagent

Use when spawning workers or offloading work: Task/subagents, specialist handoffs, A2A peers, sealed packets, coordinate/synthesize, challenge techniques (rubber-duck, interview, mimic-flow, red-team, blind review, consensus), or local Ollama one-shots to save tokens. Measuring keep/discard KPIs → octocode-graph-eval.

Overview

Publisherbgauryy
Repositoryoctocode
Skill nameoctocode-subagent
Stars
950
Forks
78
Bundled files
40
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.

  • 40 bundled files

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

  • Open source

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

Installation

Install the Octocode Subagent 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/bgauryy/octocode/tree/main/skills/octocode-subagent
  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/bgauryy/octocode.git /tmp/octocode
mkdir -p .claude/skills
cp -r /tmp/octocode/skills/octocode-subagent .claude/skills/octocode-subagent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Octocode Subagent 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 Octocode Subagent 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 Octocode Subagent 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.

Octocode Subagent

Host-agnostic delegation: cloud/host workers or local Ollama offload.
Flows: GATE → DECOMPOSE → ROUTE → PACKET → SPAWN → COORDINATE → SYNTHESIZE → CLEANUP (tool-using) · GATE → ROUTE → RUN → VERIFY → REPORT (Ollama — references/local-ollama.md).

Lobby rules

  1. Spawn only when delegation changes speed, expertise, isolation, or context quality; otherwise keep work in the parent. Default is solo; earn spawn cost first.
  2. One bounded objective per worker; no nested spawning unless the host explicitly allows it.
  3. Workers inherit no parent chat: every packet carries goal, scope, context, authority, constraints, evidence needs, and return shape.
  4. Treat worker output as claims; re-check load-bearing anchors (Ollama: always VERIFY).
  5. Barrier before synthesize — wait/list every live worker (or stop+remove); merge conflicts first; then answer.
  6. Parent owns the user, synthesis, and mutations unless a packet explicitly transfers write ownership.
  7. Pick the smallest capable configured model; declare file ownership before parallel writes.
  8. Challenge techniques use fresh context; agreement is not proof.
  9. Local Ollama is tool-less one-shot/map-reduce only — never a tool-using agent loop. Stop when solo work finishes, two High options need a winner, three angles add nothing, a user/auth gate is pending, or no live workers remain.

Smart routes — load only what the current step needs

  • When deciding solo, batch, specialist, or clean worker, load references/spawn-gate.md — delegation must earn its coordination cost.
  • When splitting work, load references/decompose.md; when choosing supervisor, pipeline, handoff, or swarm load references/patterns.md — create a dependency-aware topology.
  • Before spawning, load references/packets.md; when delegating technical research load references/octocode.md — make worker context and tool routing self-contained.
  • When selecting host model/thinking effort, load references/model-routing.md — smallest capable configured model.
  • When waiting, steering, messaging, or stopping workers, load references/coordinate.md; for independent remote peers load references/a2a.md.
  • When parallel writers share mutable state, load references/workspace.md.
  • When workers stall, fail, or conflict, load references/recovery.md; before final output load references/synthesize.md and references/output.md; at CLEANUP stop and remove every worker and release shared state per references/coordinate.md.
  • When grounding orchestration guidance in sources, load references/references.md.
  • When improving this skill, prefer octocode-graph-eval; otherwise load references/improve-loop.md.

Challenge routes — fresh context per critic; agreement is not proof

  • When quality risk needs a second mind, load references/techniques.md first — it names which technique below earns the spawn.
  • When a plan needs cheap assumption surfacing without new research, load references/rubber-duck.md; when another agent’s claims need claim-by-claim falsification, load references/interview.md.
  • When a worker must follow a borrowed playbook without borrowed chat, load references/mimic-flow.md; when a design looks too clean to ship, load references/red-team.md.
  • When a critic must judge the artifact and not the author’s story, load references/blind-review.md; when one solve stays ambiguous and independent retries can cut noise, load references/consensus.md.

Local Ollama routes — tool-less one-shot / map-reduce offload only

  • When saving tokens with local Ollama (summarize/extract/…), load references/local-ollama.md — not a Task/A2A spawn path.
  • When running that offload loop end to end, load references/workflow.md — health GATE, ROUTE, RUN shards, VERIFY, REPORT what was offloaded.
  • When unsure whether offload beats solo, load references/decision-matrix.md; when the surface is unclear (research, article, code, translate, images), load references/usage-matrix.md.
  • When selecting Ollama tags, load references/model-selection.md; when an installed family needs special flags or two families tie, load references/family-playbooks.md.
  • When writing the sealed packet, load references/packet-contract.md; for the example JSON schemas it references, load references/packet-schemas.md.
  • When inventorying models or debugging CLI behavior, load references/ollama-cli.md; for ollama run flags, non-interactive patterns, and HTTP equivalents load references/ollama-cli-run.md; for script invoke and serving knobs load references/ollama-invoke.md.
  • Before integrating any worker output, load references/verify-gate.md — pass, one tighter packet, one cascade, or solo; never silent-accept.
  • When the question is RAM kits, catalog, or MCP/tools capability rather than routing, load references/ollama-local-models.md — pull commands per RAM in references/ollama-local-models-kits.md, capability rows in references/ollama-local-models-matrix.md, cloud/heavy tags plus a sample inventory in references/ollama-local-models-heavy.md, evidence and links in references/ollama-local-models-sources.md.

Related routes

  • Use octocode-research for worker evidence; octocode-graph-eval to judge worker quality (subagent measurement cookbooks live there).
  • Use octocode-rfc-generator before changing a multi-agent architecture; octocode-prompt-optimizer for packet contracts; octocode-skills when changing this folder.

Scripts

  • Run scripts/ollama-health.sh at GATE, then again with --model "$OLLAMA_WORKER_MODEL" after ROUTE — daemon or tag missing means stay solo.
  • Run scripts/ollama-worker.sh once per sealed packet or shard at RUN — --job, --input, --schema, --out, --keepalive.

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 Octocode Subagent AI skill do?

Use when spawning workers or offloading work: Task/subagents, specialist handoffs, A2A peers, sealed packets, coordinate/synthesize, challenge techniques (rubber-duck, interview, mimic-flow, red-team, blind review, consensus), or local Ollama one-shots to save tokens. Measuring keep/discard KPIs → octocode-graph-eval.

Why use Octocode Subagent on TypingMind?

Because you install it once and use it with any model. Octocode Subagent 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 Octocode Subagent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bgauryy/octocode/tree/main/skills/octocode-subagent. 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 Octocode Subagent?

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 Octocode Subagent?

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

Is the Octocode Subagent AI skill free?

Yes. It is published on GitHub by bgauryy 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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