Deliver Spec logo

Deliver Spec

CommunityPopular
chrisbanes
deliver-spec

Use when explicitly asked to deliver an approved GitHub or local spec through a reviewed pull request, or when a Project controller delegates one claimed implementation ticket.

Overview

Publisherchrisbanes
Repositoryskills
Skill namedeliver-spec
Stars
1.1K
Forks
53
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Deliver Spec 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/chrisbanes/skills/tree/main/skills/deliver-spec
  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/chrisbanes/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/deliver-spec .claude/skills/deliver-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deliver Spec 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 Deliver Spec 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 Deliver Spec 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.

Deliver spec

Core principle

Deliver one approved outcome with proportional validation and independent final review. The lead owns architecture, integration and delivery, and may implement tightly coupled work directly. Delegate when independent work, isolation or specialist investigation justifies the overhead. Preserve explicit ownership, external-action permissions, execution budgets and invocation-specific merge authority. Never select work from a Project queue.

Select the mode

For an active run-github-project controller's verified ticket handoff, read Project controller handoff and apply its procedure instead of the standalone approval, merge, and timeout rules below. The Project controller retains all shared state and merge authority. For a direct named-spec request, use the standalone procedure below; if a Project run already owns that issue, return it to that controller.

Procedure

  1. Resolve the named source, repository, approved contract, and any existing branch or PR from fresh state. On resume, reconcile the PR, exact head, checks, feedback, and authority before acting. For integration amendments, use Evidence and review to verify the installed planner contract and retained work before consuming any delta.
  2. For a GitHub issue missing ready-for-agent, use triage and wait for its approved outcome; do not change labels yourself. Local sources skip GitHub triage but need explicit stakeholder approval. Reuse a current to-plan plan, or invoke to-plan and honor its readiness and publication gates. Approval covers the accepted contract and only the live qualification allowances it explicitly records; do not infer extra attempts or authority.
  3. Verify scope and boundaries once. A verified handoff authorizes continuing in-scope implementation, testing and repairs in the isolated checkout; require no per-file or per-stage permission packets. Reuse valid plans, reviews and checks. Require earlier independent review only for a concrete risk that must be settled before proceeding, such as freezing experimental labels before observing outputs. Establish reviewer capability from runtime metadata, never by spawning a capability-only reviewer. If final review cannot run, report that delivery blocker without waiving review.
  4. Follow the bundled implementation procedure for solo or delegated work, ownership, isolation and recovery. Reconcile retained work before implementing; never redo accepted work for routing. Follow Evidence and review for testing, corrections and the single reusable delivery record. When the source requires asynchronous or persistent live qualification, apply Live qualification and failure recovery before attempts and resumes; do not add it to ordinary offline work.
  5. Self-review the integrated candidate, run required checks, then use one independent read-only reviewer per delivery PR against requirements, repository standards and the complete diff. Use an investigator in review mode or equivalent without inherited implementation context. Reuse valid independent coverage; do not duplicate a plan or implementation review. Require evidence-backed findings and a ship, fix-first, or rethink verdict. Honor explicitly invoked provider contracts and report missing coverage. Return findings to the implementation owner, renew affected checks and review changed ranges and interactions. Broaden only when impact cannot be bounded or an explicit requirement demands it. Confirm combined coverage of the final clean head before pushing. Create and verify one draft PR for a nonempty integrated diff, or reuse the existing PR. Use closing links only for verified GitHub issues.
  6. Use shepherd for CI, feedback and PR operations. Keep repairs with their implementation owner and apply the same proportional validation and focused repair review. Routine implementation or fixture corrections preserving requirements, coverage, architecture and authority proceed under the existing approval; record them briefly. Material changes require the appropriate decision and replan before affected work continues. Weakened qualification or expanded permissions are material. Merge only with explicit authority valid for this invocation. After about 30 minutes of pending gates, hand back the exact pending checks, feedback, PR and head for later resume.

Finish gate

Report ready only when the final head passes applicable checks and reviews with findings resolved and evidence applicable to that clean head; merged only after authorized merge readback; no-change only when implementation and verification finish with an empty final diff; pending with exact outstanding gates; otherwise blocked with the needed decision or capability. Preserve the PR and source pointers on pending or blocked runs. Do not report a prior head as the reviewed result.

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 Deliver Spec AI skill do?

Use when explicitly asked to deliver an approved GitHub or local spec through a reviewed pull request, or when a Project controller delegates one claimed implementation ticket.

Why use Deliver Spec on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chrisbanes/skills/tree/main/skills/deliver-spec. 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 Deliver Spec?

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 Deliver Spec?

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

Is the Deliver Spec AI skill free?

Yes. It is published on GitHub by chrisbanes 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇