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App Evolver

CommunityPopular
daymade
app-evolver

Improves an existing application using its code, product contract, authorized work history and competitor evidence. Use for 取长补短、取其精华去其糟粕、参考竞品优化应用 or evolving an app from successful and failed work. Carries authorized changes through implementation and user-result verification; use skill-evolver for Skills.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill nameapp-evolver
Stars
1.5K
Forks
225
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the App Evolver 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/daymade/claude-code-skills/tree/main/app-evolver
  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/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/app-evolver .claude/skills/app-evolver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Evolver 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 App Evolver 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 App Evolver 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.

App Evolver

Improve the application's actual user result. Extract transferable mechanisms, resolve their fit with the current product, implement authorized improvements, and verify them through the application's consuming surface.

Use this as an orchestration Skill. Keep repository engineering, research, history access and release operations with their existing owners. For a Skill target use skill-evolver; for competitor intelligence alone use competitors-analysis; for a new product specification use the available requirements workflow. Do not replace those owners with another execution engine.

1. Bind the application and unfinished result

Before research or edits, establish:

  • Exact application, repository/ref, platform and runtime or deployment identity.
  • Original user request, current product contract and active stage; distinguish long-term positioning from the work selected now.
  • User-visible improvement, falsifiable acceptance evidence, authorized deliverables, protected behavior/data and stopping condition.
  • Analysis-only versus implementation authorization, and any unresolved product choice whose answer changes the outcome.

Reuse earlier user decisions; ask in the current conversation only for missing information or a real unresolved choice. Use the host's question tool when available. State the evidence, alternatives, consequences and recommendation. Do not delegate a user decision through another agent.

For multiple applications, bind each contract and acceptance path separately. A successful design, permission or measurement in one app is an unverified proposal in another. Do not transfer a desktop contract to its mobile sibling without checking the sibling's current contract.

Read repository instructions and relevant domain Skills. Pin the code used for judgment to an immutable ref; inspect current runtime/config separately. Preserve other writers' work and coordinate overlapping ownership before editing. On resume, reconcile the latest request, actual artifacts and unfinished evidence; continue from the first unmet result rather than replaying the original plan.

2. Ground opportunities and constraints

Read evidence and fit before selecting changes.

Inspect the existing user journey, assets, services, tests and implementation owners first. Search available prior work and personal collections when requested or required by the user's environment. Perform explicitly requested research lanes; do not silently replace deep research or history mining with a quick read. Stop a lane once its evidence resolves the decision it was meant to inform.

Use history only within explicit authorization. Retrieve through the owning index and exact session reader, preserving user/assistant/tool roles and child-agent prompt/result links. Exclude hidden reasoning. Keep unavailable, stale or incomplete coverage explicit; do not fall back to an unbounded raw-history scan. Treat assistant conclusions as hypotheses, not user endorsement or current truth.

For competitors, inspect implementation and current primary evidence beyond a feature list. Use competitors-analysis for repository evidence and the available research owner for non-repository products. Resolve revisions and distinguish observed behavior from claims. Treat source access, architectural inspiration, code reuse and distribution permission as separate questions; reading a repository does not authorize copying its implementation.

3. Decide what transfers

For each mechanism that could change the next action, record:

Decision inputRequired answer
EvidenceExact source/ref and observed normal, failure or recovery behavior
User benefitWhich current user result improves, and how it can be falsified
Existing equivalentWhat the app already provides and where it differs
Fit and costContract, lifecycle, data, defaults, dependencies and operational cost
DispositionAdopt, adapt, reject or needs verification, with the decisive reason
Next evidenceSmallest probe or journey that resolves remaining uncertainty

Keep positive opportunities even when no existing defect has been demonstrated. Do not invent failures to justify them. Reject mechanisms that contradict the current contract; preserve useful parts without importing the competitor's whole architecture, business model or weaker guarantees. A failed parameter family is not a reason to try indefinitely; identify what structurally new evidence would justify another attempt.

When the user requests a team, use the host's actual collaboration tools and bounded roles: product fit, implementation/lifecycle and independent challenge as needed. Give original requirements, exact evidence, distinct ownership and falsifiable probes. Treat findings as hypotheses until the executor checks their sources or runs the probes. Without such authorization, use the minimum required independent review and do not start a research or evaluation fan-out.

Resolve researchable uncertainty yourself. Ask the user about preferences, mutually exclusive product choices or missing authorization, not facts available in code or a read-only probe. A missing optional research lane does not block independent authorized work; a missing load-bearing contract blocks its dependent change until resolved.

4. Implement the smallest useful increment

Read implementation and acceptance before editing or launching validation.

Route implementation to the repository's actual engineering workflow and domain owner, not Skill Creator. If an optional Skill is unavailable, use accessible repository instructions and applicable tools within authorization; do not auto-install private dependencies or pretend their checks ran.

Order work by user benefit and dependencies. Preserve protected behavior, source provenance, persistence, identity and recovery paths. Keep optional work out of a mandatory hot path when the product contract requires independent completion; choose its limits from this app's workload and measurements. Do not copy a timeout or device policy from an unrelated app.

Complete requested implementation and necessary verification; do not stop at a plan or recommendation when the user asked to optimize the application. Keep an analysis-only request read-only. Respect the authorized boundary for deployment, production writes, spending, publication and destructive operations.

5. Verify, correct and deliver

Verify the intended consumer's outcome and protected healthy cases. Separate source checks, synthetic tests, real runtime journeys, packaged/installed behavior, production state and human adoption. Evidence at one layer does not establish another. Read back at the point where the result is consumed.

When evidence fails, inspect raw signals, seek a falsifier and correct the root cause or the mistaken criterion. Retry only after a relevant input or hypothesis changes. Calibrate new guards/checks on healthy and failing inputs; account for examined items and demonstrate that the intended bad behavior is detected. Do not weaken a product guarantee merely to turn a check green.

Finish repository delivery through its established authorized workflow. Update only changed facts in existing product SSOTs. Keep reusable lessons with their owning Skills when requested; do not create memory files, hooks or new processes as an automatic by-product.

Report what changed, why it fits, where it landed, what user-result evidence passed and what remains unverified. Request human acceptance when the declared outcome depends on the user's judgment; never mark it passed on their behalf. Stop when the authorized result and necessary evidence are complete. If only an external condition remains, name the unfinished result and exact resume condition.

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 App Evolver AI skill do?

Improves an existing application using its code, product contract, authorized work history and competitor evidence. Use for 取长补短、取其精华去其糟粕、参考竞品优化应用 or evolving an app from successful and failed work. Carries authorized changes through implementation and user-result verification; use skill-evolver for Skills.

Why use App Evolver on TypingMind?

Because you install it once and use it with any model. App Evolver 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 App Evolver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/app-evolver. 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 App Evolver?

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 App Evolver?

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

Is the App Evolver AI skill free?

Yes. It is published on GitHub by daymade 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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