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Octocode Research

Community
bgauryy
octocode-research

Use when code must be checked, not assumed: trace connections (callers, imports, cross-repo wiring, what breaks if I change it), locate behavior, map a system, diagnose a failure, RCA. Also for external repositories, npm packages, upstream/prior art, and general research. Plan a coding flow before writing, validate it after. Triggers on 'research this' or 'use octocode'. Gives exact file:line/PR/commit evidence with confidence. Skip trivial edits whose blast radius is already known. Not for authoring or copyediting the docs themselves, or for building skill folders: docs deliverable → octocode-documentation; SKILL.md folders → octocode-skills.

Overview

Publisherbgauryy
Repositoryoctocode
Skill nameoctocode-research
Stars
950
Forks
78
Bundled files
22
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.

  • 22 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 Research 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-research
  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-research .claude/skills/octocode-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Octocode Research 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 Research 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 Research 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 Research

Evidence before assertion. Find the anchor, read the exact bytes, prove the claim, then answer or patch.

text
FRAME → CLASSIFY → MODEL → SEARCH → READ EXACT → PROVE → DECIDE/PATCH → VERIFY

That's the full shape, not a checklist to march through. Scale it to the claim: a small lookup gets a cheap read and an honest confidence label; a delete, a merge verdict, or a root cause earns the whole ladder. Skip stages you can already answer — but say which ones you skipped.

The rules

  1. Open with one line: corpus, actual vs desired, task class, mode, surfaces used and skipped.
  2. Call it a bug only when evidence shows a supported contract was violated.
  3. Root cause needs mechanism, trigger, violated contract, divergence boundary, and a killed alternate.
  4. Use the strongest handle you already hold. For nontrivial claims check two of: structure, stream, connections.
  5. A snippet is a lead. Empty means this lane can't see it, not it isn't there. Say which you mean.
  6. Track claim → evidence → confidence → next check. Cite exact anchors, and only checks that actually ran.
  7. Ask before broad contracts, deletes/renames, thin evidence, or a third unrelated search space. Patch after proof.

Stop when: grounded evidence answers the framed question and the alternate is dead; no cheap next step can change the conclusion; the budget is hit (default 3-5 decisive iterations or ~15 minutes); the last iterations changed no state; retries stay thin, or a license/product/architecture call belongs to the user; a gate blocks (broad contract, delete/rename, clone or run untrusted code, unapproved artifact write); or a skill edit measured flat/worse — revert through references/improve-loop.md. Report the remaining gaps instead of padding certainty.

Workflows

Start with references/algorithm.md (routing, evidence grades) and references/problem-framing.md (is this a bug, feature, enhancement, or still unknown?). Then pick one route by what you're looking at — load references/workflows.md when you need the per-route detail, the load budget per task size, or the handoff receipt between routes:

SituationRoute
This repo, checkout, installed dependencyreferences/workflow-local.md
External repository, npm package, upstream projectreferences/workflow-external.md
Cross-repo connections, local clue → upstream, or remote code needing AST/LSP proofreferences/workflow-combination.md
Rank an ecosystem — several candidate repos/packagesreferences/github-landscape.md
Something fails and you need the causereferences/workflow-debug.md
Plan a coding flow, implement, migrate, patch behaviorreferences/workflow-change.md
Reshape structure or names, keep behaviorreferences/workflow-refactor.md
Validate after a change; review a PR or local diffreferences/workflow-pr-review.md
Trace connections — callers, imports, references, reachabilityreferences/code-research.md

Proof depth for any of them: references/code-research.md. General research — Map / Validate / Investigate / Plan across code, packages, docs, and history: references/research-flow.md.

Review runs in three parts: use references/workflow-pr-review.md for target, guidelines, and risk sizing; then references/workflow-pr-review-analysis.md for sizing depth, flow proof, and finding shape; then references/workflow-pr-review-report.md for the verification-gated recommendation and the optional written document.

Reach for these only when they earn it: references/loop-mode.md (evidence keeps flipping), references/long-research.md (durable decision brief), references/researcher-mindset.md (budgets, fan-out, campaign planning).

Load a reference when the current step needs it. Loading all of them is a failure mode.

Tooling

Prefer Octocode MCP tools when exposed. Otherwise npx octocode tools <name> — same 15 tools, same schemas, no loss.

bash
npx octocode context --minimal                         # what's available
npx octocode tools <name> --scheme --json --compact    # read fields — never guess
npx octocode tools <name> --queries '<json>' --compact # run it

Batch up to five queries per call. Orient cheap (tree, discovery) before exact reads. Follow returned next.* and cursors instead of re-deriving them.

Read references/octocode.md when transport, tool choice, auth, gates (ENABLE_LOCAL, ENABLE_CLONE, ENABLE_RELEASES), materialization, diagnostics, or exit codes are unclear.

Output

Finding · Evidence · Confidence · Next. Decisions add verdict, risks, exact anchors, verification, and the smallest safe fix. Report gaps instead of padding certainty.

Related

octocode-brainstorming (worth building?) · octocode-rfc-generator (design contract) · octocode-graph-eval (goal→KPI) · octocode-documentation (docs deliverable) · octocode-skills (skill folders) · octocode-subagent (fan-out) · octocode-roast (critique tone).

When changing this skill, run node scripts/check-description.mjs (description contract; --help for flags) and gate accept/revert with references/improve-loop.md.

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 Research AI skill do?

Use when code must be checked, not assumed: trace connections (callers, imports, cross-repo wiring, what breaks if I change it), locate behavior, map a system, diagnose a failure, RCA. Also for external repositories, npm packages, upstream/prior art, and general research. Plan a coding flow before writing, validate it after. Triggers on 'research this' or 'use octocode'. Gives exact file:line/PR/commit evidence with confidence. Skip trivial edits whose blast radius is already known. Not for authoring or copyediting the docs themselves, or for building skill folders: docs deliverable → octoc...

Why use Octocode Research on TypingMind?

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

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

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 Research?

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

Is the Octocode Research 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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