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Find Prompt

Community
Rtur2003
find-prompt

Use when the user names a coding or Claude Code task ("add OAuth", "set up hooks", "review this PR", "pick a model"), asks which prompt to use, says to "use the right prompt", or when you enter this repo and need to orient without reading every file.

Overview

PublisherRtur2003
RepositoryClaude-Code-Promts-Skills
Skill namefind-prompt
Stars
56
Forks
16
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 Rtur2003 on GitHub. Read the source before you install it.

Installation

Install the Find Prompt 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/Rtur2003/Claude-Code-Promts-Skills/tree/main/.claude/skills/find-prompt
  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/Rtur2003/Claude-Code-Promts-Skills.git /tmp/Claude-Code-Promts-Skills
mkdir -p .claude/skills
cp -r /tmp/Claude-Code-Promts-Skills/.claude/skills/find-prompt .claude/skills/find-prompt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Find Prompt 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 Find Prompt 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 Find Prompt 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.

Find the right prompt and run with it

You route the current task to the right file(s) in this library, read them yourself, and then continue the existing work under those instructions — not from scratch, but picking up the conversation and any task already in progress.

${CLAUDE_SKILL_DIR} is this skill's own directory (<library-root>/.claude/skills/find-prompt/). The library root is ${CLAUDE_SKILL_DIR}/../../... All prompt files are under ${CLAUDE_SKILL_DIR}/../../../prompts/english/. Never hardcode an absolute path — always build it from ${CLAUDE_SKILL_DIR} so this works for anyone who clones the library, wherever they put it.

If ${CLAUDE_SKILL_DIR} is not substituted (it shows up literally), fall back: the skill sits next to SKILL.md; run ls up the tree from your working directory to find the folder containing prompts/english/agents/claude-agent-system-prompt.md.

Steps

  1. Determine the task. Use $ARGUMENTS if given. Otherwise use the task already being worked on in this conversation (the user's last real request, the in-progress work, the files open). If genuinely unclear, ask one short question and stop.

  2. Match the task to the routing table below. Pick the base prompt plus specialists by tier: 1 for a single-domain task (default), 2 only if the task spans two genuinely independent domains, or route to Multi-Agent Orchestration if the work needs isolation/review. See ${CLAUDE_SKILL_DIR}/../../../prompts/english/workflows/prompt-selector-guide.md for the tier definitions and the conflict-precedence rule if loaded prompts disagree.

  3. Read the file(s) with the Read tool, using paths built from ${CLAUDE_SKILL_DIR}:

    • base: ${CLAUDE_SKILL_DIR}/../../../prompts/english/agents/claude-agent-system-prompt.md
    • specialist: ${CLAUDE_SKILL_DIR}/../../../prompts/english/<path from the table> If a path does not exist, tell the user to run /update-prompts and stop.
  4. Adopt them and continue. Treat the base prompt as your operating system prompt and the specialist as the domain protocol for this task. Then:

    • Restate the current task in one line (from the conversation, not invented).
    • State the success criteria you'll hold yourself to.
    • Carry over every session constraint already established (no git, no server start, static-only, don't touch X, etc.).
    • Pick up the work where it stands — continue the in-progress task under the new protocol. Do not restart, do not re-ask what was already answered, do not re-explore what was already explored.
  5. Confirm in one short block which prompts you loaded and what you're now doing, then proceed.

Routing table

Paths are relative to prompts/english/.

Running Claude Code itself (base prompt not needed — the Claude Code prompt is enough alone)

Task is about…Load
writing / debugging a skill, SKILL.mdagents/agent-skills-prompt.md
connecting a DB / browser / API / tool via MCPagents/mcp-integration-prompt.md
a plugin, a marketplace, bundling/sharing configagents/claude-code-plugins-prompt.md
a hook, running something on edit/commit/stop, blocking a commandagents/hooks-automation-prompt.md
subagents, parallel agents, dynamic workflows, /batch, whole-codebase audit, writer/revieweragents/multi-agent-orchestration-prompt.md
CLAUDE.md, settings.json, .claude/rules, permissionsagents/claude-code-workflow-prompt.md
effort levels, ultrathink, plan mode, how much to thinkagents/claude-code-modes-prompt.md
which model to use, Opus vs Sonnet vs Haiku vs Fable, pricingworkflows/model-selection-guide.md
what a current Claude Code build can do — plan mode, rewind, headless, surfacesworkflows/claude-code-native-features-guide.md
building an agent in Python/TS, the Agent SDK, query()workflows/agent-sdk-guide.md
AGENTS.md, one config across Cursor/Codex/Aider, the primary source for a claimworkflows/reference-resources.md
evaluating or installing a skill, plugin, MCP server, or agent toolkitworkflows/ecosystem-discovery-guide.md

Building software (base = Agent System + the one specialist below)

Task is about…Specialist
web frontend — React/Vue/Angular/Svelte, a page, a componentproject-types/web-development-prompt.md
a REST/GraphQL/gRPC API, an endpoint, a contractproject-types/api-development-prompt.md (+ agents/api-design-graphql-prompt.md for schema-first design)
an end-to-end app, frontend + backend togetheragents/fullstack-development-prompt.md
iOS / Android / React Native / Flutter / KMPproject-types/mobile-development-prompt.md
a desktop app — Tauri / Electron / MAUI / Qtproject-types/desktop-development-prompt.md
a data pipeline — ETL, streaming, Kafka, Airflow, dbtagents/data-engineering-prompt.md
an ML model, training, RAG, an LLM featureproject-types/data-science-ml-prompt.md (+ agents/ai-llm-integration-prompt.md for app-side integration)
Kubernetes, CI/CD, Terraform/OpenTofu, Docker, Cloudflareproject-types/devops-cicd-prompt.md (+ agents/cloud-infrastructure-prompt.md for multi-region / IaC depth)
a database schema, SQL, indexing, a slow queryagents/database-optimization-prompt.md (+ project-types/database-sql-prompt.md for data modeling)
a game — Unity / Unreal / Godot / Bevy, netcodeproject-types/game-development-prompt.md
firmware, embedded, IoT, an MCU, an RTOSproject-types/embedded-iot-prompt.md
a smart contract, Solidity, web3, an L2project-types/blockchain-web3-prompt.md
anything else / language-agnosticproject-types/general-software-development-prompt.md

Improving existing code (base = Agent System + the one specialist below)

Task is about…Specialist
reviewing a PR or change setagents/code-review-prompt.md. Also load agents/ui-design-systems-prompt.md if the change touches UI, or agents/security-audit-prompt.md if it touches auth/data. If git is available and you can see the diff, /code-review (the bundled skill) is an option too.
security — a vulnerability, threat model, auth, secretsagents/security-audit-prompt.md
a production incident, "it's broken", root cause, a bugagents/debugging-troubleshooting-prompt.md
reducing complexity or technical debt, cleaning upagents/refactoring-prompt.md
adding tests, a test strategy, coverage, a flaky testagents/testing-strategies-prompt.md
slowness — latency, throughput, cost, memory, bundle sizeagents/performance-optimization-prompt.md
migrating a framework/runtime/DB, a major version bumpagents/migration-upgrade-prompt.md
logs, metrics, traces, alerting, observabilityagents/monitoring-observability-prompt.md
fault tolerance — retries, circuit breakers, resilienceagents/error-handling-resilience-prompt.md
accessibility — WCAG, screen readers, a11yagents/accessibility-audit-prompt.md

Deciding (base = Agent System + the one specialist below)

Task is about…Specialist
system design, an architecture pattern, trade-offsagents/architecture-patterns-prompt.md
choosing a tool or library — "what should I use for X"agents/technology-stack-prompt.md
GDPR / HIPAA / SOC 2 / PCI, regulated scopeagents/compliance-governance-prompt.md
branching, commits, the release processagents/git-version-control-prompt.md
DX, linting, onboarding, the dev environmentagents/developer-experience-tooling-prompt.md
a monorepo, multi-package coordination, Turborepo/Nxagents/monorepo-complex-projects-prompt.md
writing docs, a README, API referenceagents/documentation-prompt.md
a design system, tokens, a component library, themingagents/ui-design-systems-prompt.md
consequential research, source verification, product/service comparison, recommendation beyond codingagents/evidence-driven-research-prompt.md

Rules

  • Composition by tier, not a hard cap. Tier 1 (base + 1 specialist) covers most tasks. Move to Tier 2 (base + 2 specialists) only when the task genuinely spans two independent domains — say so explicitly. Route to Multi-Agent Orchestration (Tier 3) when the work needs isolated workers or adversarial review.
  • The base is always agents/claude-agent-system-prompt.md, except for a task purely about running Claude Code (then the single Claude Code prompt is enough).
  • Tiny task (typo, rename, one-line fix): don't load anything — say so and do it.
  • Continue, don't restart. After loading, resume the conversation's in-progress work under the new instructions. Keep every fact, decision, and constraint already established.
  • If nothing matches cleanly, read ${CLAUDE_SKILL_DIR}/../../../prompts/english/INDEX.md and route from its task table.

Confirmation block (output this, then keep working)

Loaded: agents/claude-agent-system-prompt.md + <specialist path>   [+ <optional second>]
Task (continuing): <one line, from the conversation>
Success criteria: <measurable>
Constraints carried over: <list, or "none">

Then proceed with the task under the loaded protocol — from where it stands, not from zero.

Frequently asked questions

What does the Find Prompt AI skill do?

Use when the user names a coding or Claude Code task ("add OAuth", "set up hooks", "review this PR", "pick a model"), asks which prompt to use, says to "use the right prompt", or when you enter this repo and need to orient without reading every file.

Why use Find Prompt on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Rtur2003/Claude-Code-Promts-Skills/tree/main/.claude/skills/find-prompt. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Find Prompt?

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 Find Prompt?

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

Is the Find Prompt AI skill free?

Yes. It is published on GitHub by Rtur2003 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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