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Octocode Prompt Optimizer

Community
bgauryy
octocode-prompt-optimizer

Use when an agent prompt, tool schema, policy, or handoff needs to get clearer, safer, easier to trigger, cheaper in context, or measurable against real behavior. For SKILL.md folder install/review/structure, use octocode-skills.

Overview

Publisherbgauryy
Repositoryoctocode
Skill nameoctocode-prompt-optimizer
Stars
950
Forks
78
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 bgauryy on GitHub. Read the source before you install it.

Installation

Install the Octocode Prompt Optimizer 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-prompt-optimizer
  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-prompt-optimizer .claude/skills/octocode-prompt-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Octocode Prompt Optimizer 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 Prompt Optimizer 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 Prompt Optimizer 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 Prompt Optimizer

Optimize instruction behavior, not prose aesthetics.

Flow: READ → UNDERSTAND → RATE → FIX → VALIDATE → OUTPUT.

Lobby rules and gates

  • READ: inspect the whole input and its type; UNDERSTAND: map goal, parts, flow, assumptions, and unknowns.
  • RATE: record evidenced issues, severity, and baseline; FIX: address Critical/High issues and name deliberate deferrals.
  • VALIDATE: prove intent and required behavior remain correct; OUTPUT: provide the requested artifact and truthful delta.
  • Use the full path for multi-section, ambiguous, tool-facing, or high-risk work; combine adjacent steps only for short, low-risk text. Never skip VALIDATE.
  • Preserve intent, working branches, identifiers, commands, and required metadata; ask before changing them.
  • Verify cited commands, flags, paths, tool names, and schemas before rewriting; flag unverified claims.
  • Make only critical behavior mandatory; retain preference language for real preferences. Mutate files only with authority.
  • Stop when: a material unknown would change intent, scope, or risk (ask one focused question and pause); instruction authority is ambiguous, or resolving a conflict would override user intent; an edit changed intent or working logic (revert it and return to UNDERSTAND); write authority is missing (deliver a patch-style delta instead of a file change); a VALIDATE check fails twice on the same section (report the weakest branch instead of forcing a pass); a reliability gain has no held-out evidence (report it as unmeasured).

Smart routes — load only what the current step needs

  • READ and UNDERSTAND: load references/gates.md — read every section and map intent before judging or drafting.
  • RATE: load references/rate.md; FIX: load references/fix.md; VALIDATE: load references/validate.md; OUTPUT: load references/output.md — load only the active gate so later-step advice cannot bias the current decision.
  • When instructions conflict or a fix needs a compact instruction pattern, load references/patterns.md — apply the higher authority and log the resolution in one line.
  • When reducing noise, load references/conciseness-toolkit.md; when fixing priority/hierarchy load references/attention.md; when choosing a technique for an observed failure load references/prompt-techniques.md — match technique to failure mechanism.
  • When optimizing tool or MCP contracts, load references/tool-contracts.md; for agent handoffs load references/agent-communication.md; for typed packet boundaries load references/zod-agent-contracts.md — make inputs, outputs, authority, and failure states explicit.
  • When context can overflow, load references/context-budget.md; when repeated calls share stable prefixes load references/prompt-caching.md — control relevance, pagination, latency, and cost.
  • When reliability must be measured, load references/evaluation-data.md — build realistic held-out scenarios, verifiers, metrics, and a failure ledger.
  • When instructions consume retrieved or user-supplied content, load references/untrusted-content.md — preserve the boundary between data and authority.
  • When improving this skill, prefer octocode-graph-eval; otherwise load references/improve-loop.md — require measurable acceptance instead of intuition.

Related routes

  • Use octocode-skills for skill-folder architecture/review; octocode-research to verify cited contracts; octocode-graph-eval for held-out behavior.
  • Use octocode-subagent for delegation topology.

Done gate

  • This skill ships no scripts: every gate above is model-driven, so never report a check you did not actually perform.
  • Done requires VALIDATE passed, the OUTPUT variant matching the request, and the reported before/after score, changed files, and deferrals all matching reality.

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 Prompt Optimizer AI skill do?

Use when an agent prompt, tool schema, policy, or handoff needs to get clearer, safer, easier to trigger, cheaper in context, or measurable against real behavior. For SKILL.md folder install/review/structure, use octocode-skills.

Why use Octocode Prompt Optimizer on TypingMind?

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

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

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 Prompt Optimizer?

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

Is the Octocode Prompt Optimizer 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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