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Acceptance Orchestrator

Community
diegosouzapw
acceptance-orchestrator

Acceptance Orchestrator workflow skill. Use this skill when the user needs a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

Overview

Publisherdiegosouzapw
Repositoryawesome-omni-skills
Skill nameacceptance-orchestrator
Stars
145
Forks
32
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 diegosouzapw on GitHub. Read the source before you install it.

Installation

Install the Acceptance Orchestrator 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/diegosouzapw/awesome-omni-skills.git /tmp/awesome-omni-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-omni-skills/skills/acceptance-orchestrator .claude/skills/acceptance-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acceptance Orchestrator 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 Acceptance Orchestrator 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 Acceptance Orchestrator 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.

Acceptance Orchestrator

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/acceptance-orchestrator from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Acceptance Orchestrator

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Required Sub-Skills, Inputs, State Machine, Stop Conditions, Human Gates, Output Contract.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

  • The task already has an issue or clear acceptance criteria and should run end-to-end with minimal human re-intervention.
  • You need structured handoff across implementation, review, deployment, and final verification.
  • You want explicit stop conditions and escalation instead of silent partial completion.
  • Use when the request clearly matches the imported source intent: a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
  • Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
  • Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

SituationStart hereWhy it matters
First-time usemetadata.jsonConfirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance reviewORIGIN.mdGives reviewers a plain-language audit trail for the imported source
Workflow executionSKILL.mdStarts with the smallest copied file that materially changes execution
Supporting contextSKILL.mdAdds the next most relevant copied source file without loading the entire package
Handoff decision## Related SkillsHelps the operator switch to a stronger native skill when the task drifts

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

  1. Intake
  2. Read issue and extract task goal + DoD.
  3. Issue gate
  4. Use create-issue-gate logic.
  5. If issue is not ready or execution gate is not allowed, stop immediately.
  6. Do not implement anything while issue remains draft.
  7. Execute

Imported Workflow Notes

Imported: Workflow
  1. Intake

    • Read issue and extract task goal + DoD.
  2. Issue gate

    • Use create-issue-gate logic.
    • If issue is not ready or execution gate is not allowed, stop immediately.
    • Do not implement anything while issue remains draft.
  3. Execute

    • Hand off to closed-loop-delivery for implementation and local verification.
  4. Review loop

    • If PR feedback is relevant, batch polling windows as:
      • wait 3m
      • then 6m
      • then 10m
    • After the 10m round, stop waiting and process all visible comments together.
  5. Deploy and runtime verification

    • If DoD depends on runtime behavior, deploy only to dev by default.
    • Verify with real logs/API/Lambda behavior, not assumptions.
  6. Completion gate

    • Before any claim of completion, require verification-before-completion.
    • No success claim without fresh evidence.
Imported: Overview

Orchestrate coding work as a state machine that ends only when acceptance criteria are verified with evidence or the task is explicitly escalated.

Core rule: do not optimize for "code changed"; optimize for "DoD proven".

Imported: Required Sub-Skills
  • create-issue-gate
  • closed-loop-delivery
  • verification-before-completion

Optional supporting skills:

  • deploy-dev
  • pr-watch
  • pr-review-autopilot
  • git-ship

Examples

Example 1: Ask for the upstream workflow directly

text
Use @acceptance-orchestrator to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

text
Review @acceptance-orchestrator against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

text
Use @acceptance-orchestrator for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

text
Review @acceptance-orchestrator using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

  • Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
  • Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
  • Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
  • Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
  • Treat generated examples as scaffolding; adapt them to the concrete task before execution.
  • Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/acceptance-orchestrator, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

  • @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
  • @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
  • @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
  • @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Resource familyWhat it gives the reviewerExample path
referencescopied reference notes, guides, or background material from upstreamreferences/n/a
examplesworked examples or reusable prompts copied from upstreamexamples/n/a
scriptsupstream helper scripts that change execution or validationscripts/n/a
agentsrouting or delegation notes that are genuinely part of the imported packageagents/n/a
assetssupporting assets or schemas copied from the source packageassets/n/a

Imported Reference Notes

Imported: Inputs

Require these inputs:

  • issue id or issue body
  • issue status
  • acceptance criteria (DoD)
  • target environment (dev default)

Fixed defaults:

  • max iteration rounds = 2
  • PR review polling = 3m -> 6m -> 10m
Imported: State Machine
  • intake
  • issue-gated
  • executing
  • review-loop
  • deploy-verify
  • accepted
  • escalated
Imported: Stop Conditions

Move to accepted only when every acceptance criterion has matching evidence.

Move to escalated when any of these happen:

  • DoD still fails after 2 full rounds
  • missing secrets/permissions/external dependency blocks progress
  • task needs production action or destructive operation approval
  • review instructions conflict and cannot both be satisfied
Imported: Human Gates

Always stop for human confirmation on:

  • prod/stage deploys beyond agreed scope
  • destructive git/data operations
  • billing or security posture changes
  • missing user-provided acceptance criteria
Imported: Output Contract

When reporting status, always include:

  • Status: intake / executing / accepted / escalated
  • Acceptance Criteria: pass/fail checklist
  • Evidence: commands, logs, API results, or runtime proof
  • Open Risks: anything still uncertain
  • Need Human Input: smallest next decision, if blocked

Do not report "done" unless status is accepted.

Imported: Limitations
  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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 Acceptance Orchestrator AI skill do?

Acceptance Orchestrator workflow skill. Use this skill when the user needs a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

Why use Acceptance Orchestrator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/diegosouzapw/awesome-omni-skills/tree/main/skills/acceptance-orchestrator. 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 Acceptance Orchestrator?

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 Acceptance Orchestrator?

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

Is the Acceptance Orchestrator AI skill free?

Yes. It is published on GitHub by diegosouzapw 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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