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Project Development Mindset

Community
thienanblog
project-development-mindset

Plan and carry repository changes through implementation, verification, and handoff. Use for project work that needs a general development workflow; select specialist guidance when it adds task-specific value.

Overview

Publisherthienanblog
Repositoryawesome-ai-agent-skills
Skill nameproject-development-mindset
Stars
66
Forks
21
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Project Development Mindset 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.
  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/thienanblog/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/plugins/project-development-skills/skills/project-development-mindset .claude/skills/project-development-mindset
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Development Mindset 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 Project Development Mindset 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 Project Development Mindset 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.

Project Development Mindset

Deliver the requested outcome using project evidence and the smallest coherent change. Scale investigation and verification to impact, uncertainty, and reversibility.

Working agreement

Follow the user's request and applicable repository instructions over these defaults. Use existing authorization; ask only about missing decisions that materially affect scope, cost, safety, or the result. Continue independent authorized work while awaiting an answer.

Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.

Establish the task

  • Inspect applicable instructions, Git state, and the source, tests, configuration, and docs relevant to the request. Preserve unrelated user work and follow the repository's branch and freshness policy.
  • Identify observable success criteria and affected contracts. Use a short plan for work with dependencies or material uncertainty; a routine fix needs no planning ceremony.
  • Distinguish intended behavior from current behavior. Resolve stale or conflicting docs against source and runtime evidence; ask only when the remaining choice belongs to the user.
  • Keep the original objective and accepted changes in view during long work. A status question or correction usually steers the task rather than replacing it.

Implement

  • Reuse project components, dependencies, commands, and architecture when their semantics fit. Change a harmful pattern only where it affects this task.
  • Keep abstractions tied to current invariants or meaningful reuse. Avoid speculative options, new frameworks, and adjacent cleanup without a present need.
  • Inspect affected callers and consumers when changing public APIs, persisted data, permissions, or cross-project contracts.
  • Use installed versions and official documentation for uncertain external APIs. Keep credentials out of source, logs, fixtures, and output.
  • Carry authorized work through to completion. Prepare a concrete, reviewable result before requesting any still-missing approval for publication, deployment, or other consequential actions. Do not infer those actions from an ordinary implementation request.

Verify and review

  • Choose checks that can detect failures in the changed behavior, using the project's existing tools. Complete required checks and follow any explicit testing budget.
  • Add regression coverage when it protects meaningful behavior. Skip tests that merely duplicate a reversible, low-impact edit.
  • Reuse passing evidence for unchanged code and equivalent environments. Expand or repeat checks for new changes, failures, affected contracts, or unresolved risks; stop once the evidence is sufficient. Offer a broader suite only when it would resolve a concrete gap, respecting repository approval requirements.
  • For material UI changes, inspect the rendered result at relevant states and viewports under the host's browser policy. Distinguish manual browser evidence from automated E2E tests.
  • Review the complete task diff against the intended base for missing requirements, regressions, unsafe changes, and accidental files. Fix actionable findings and rerun affected checks.
  • Update durable documentation when behavior, setup, commands, ownership, or contracts change. Use existing owners instead of creating new documentation structures for routine work.

Use specialist guidance selectively

Start directly with a specialist when the task clearly calls for it. Use this general workflow when ownership is unclear or several responsibilities need coordination. Routine tests, docs edits, framework commands, and browser checks do not each need another skill.

Read quality-skill-routing.md when choosing a specialist would help, or ui-ux-concept-routing.md for visual decisions and reference matching. Load only relevant guidance; there is no fixed skill-count quota. Avoid duplicate workflows and discard obsolete phase instructions as the task changes. An unavailable optional skill should not block work the current tools can complete.

Use brainstorm-first for a requested comparison or a material decision that needs options. Use run-reviewable-subtask-loop only when explicitly requested or accepted; task size and internal subtasks do not activate it or authorize subagents.

Handoff

Lead with what changed and why. Report relevant checks and their results, remaining gaps, and any decision still needed. Compare the delivered behavior with the user's criteria; fix in-scope gaps before stopping. Describe uncertainty with evidence instead of an invented numerical confidence score.

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 Project Development Mindset AI skill do?

Plan and carry repository changes through implementation, verification, and handoff. Use for project work that needs a general development workflow; select specialist guidance when it adds task-specific value.

Why use Project Development Mindset on TypingMind?

Because you install it once and use it with any model. Project Development Mindset 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 Project Development Mindset in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thienanblog/awesome-ai-agent-skills/tree/main/plugins/project-development-skills/skills/project-development-mindset. 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 Project Development Mindset?

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 Project Development Mindset?

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

Is the Project Development Mindset AI skill free?

Yes. It is published on GitHub by thienanblog under the Apache-2.0 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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