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Implement

Community
boshu2
implement

Execute one bounded RED to GREEN experiment from bead or caller intent; return derived subject identity and check facts. Triggers: "implement", "implement this bead", "run the experiment". Full plan-to-validation requests route to rpi.

Overview

Publisherboshu2
Repositoryagentops
Skill nameimplement
Stars
450
Forks
42
Bundled files
2
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.

  • 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 boshu2 on GitHub. Read the source before you install it.

Installation

Install the Implement 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/boshu2/agentops/tree/main/packs/agentops-executor/agents/implementer-claude/skills/implement
  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/boshu2/agentops.git /tmp/agentops
mkdir -p .claude/skills
cp -r /tmp/agentops/packs/agentops-executor/agents/implementer-claude/skills/implement .claude/skills/implement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implement 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 Implement 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 Implement 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.

Implement

Execute exactly one bounded experiment described by the resolved bead or caller intent. Implement owns subject edits and factual evidence. It does not create a second planning record or a model-authored candidate packet.

Workflow

  1. Read the intent, acceptance, and scope from their existing source. A runtime may snapshot and hash that source automatically for drift detection.
  2. Run the declared first acceptance check before changing behavior. RED-first applies only when acceptance is behavioral: preserve evidence that the check fails for the expected missing behavior. Relocations, doc merges, and pure refactors need no failing-check ritual — record an honest green pre-change baseline instead.
  3. Make the smallest in-scope change that satisfies the active behavior.
  4. Run the targeted acceptance checks and capture factual results.
  5. Refactor only while those checks stay green. Refactoring does not change the acceptance test.
  6. Have the runtime derive actual changed paths and subject-manifest.v1 from the before/after subject. Do not make the model transcribe those facts.
  7. Return the manifest digest, author context ID, and exact check receipts in the response or runtime channel. Stop.

Specialists such as standards, domain, test, refactor, and security may provide advice. They are never hard dependencies and cannot add lifecycle authority.

Evidence proportionality

During edits, run the smallest deterministic checks that can falsify the active change. Reuse exact-input receipts when their subject and tool identity still match. Run an expensive full-suite check at the integration boundary, or earlier only when the intent explicitly makes it the first acceptance check. Repeatedly replaying the full suite after every focused edit adds latency, not proof.

Scope conflict rule

On discovering a live consumer of the change outside the declared write scope — a test asserting the old path, a generated twin, a gate reading the moved file — stop and report the exact file and line to the caller. Do not silently expand scope to absorb it. One repair revision of the intent is the maximum before escalating to the caller; the 2026-07-15 heal-skill fold took three intent revisions (lineage under .agents/ao/intents/sha256/26a4f2be...eb48) because hand-enumerated scope kept missing live consumers.

Before declaring GREEN, self-audit the diff for mocks, placeholders, TODO stubs, and hardcoded fixture values standing in for real behavior. A check that passes against a placeholder is not evidence for the acceptance criterion; either finish the behavior or report it as not built.

Boundary

  • Do not commit, push, claim, close, release, land, reserve, retry, or invoke a semantic validator.
  • Do not silently expand acceptance. A different acceptance contract is a new intent for a caller to start separately.
  • A failed check is evidence for the caller, not permission to create a packet or validation loop.

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 Implement AI skill do?

Execute one bounded RED to GREEN experiment from bead or caller intent; return derived subject identity and check facts. Triggers: "implement", "implement this bead", "run the experiment". Full plan-to-validation requests route to rpi.

Why use Implement on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/boshu2/agentops/tree/main/packs/agentops-executor/agents/implementer-claude/skills/implement. 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 Implement?

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 Implement?

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

Is the Implement AI skill free?

Yes. It is published on GitHub by boshu2 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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