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Astra Flash Orchestrator

Community
ethanplusai
astra-flash-orchestrator

Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.

Overview

Publisherethanplusai
Repositorycodex-on-crack
Skill nameastra-flash-orchestrator
Stars
752
Forks
73
Bundled files
14
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.

  • 14 bundled files

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

  • Open source

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

Installation

Install the Astra Flash 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.
    https://github.com/ethanplusai/codex-on-crack/tree/main/legacy/astra-flash-orchestrator/skill/astra-flash-orchestrator
  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/ethanplusai/codex-on-crack.git /tmp/codex-on-crack
mkdir -p .claude/skills
cp -r /tmp/codex-on-crack/legacy/astra-flash-orchestrator/skill/astra-flash-orchestrator .claude/skills/astra-flash-orchestrator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Astra Flash 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 Astra Flash 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 Astra Flash 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.

Astra plans. Flash implements. Astra accepts.

Use the existing Codex Router, not a second agent CLI or API client. This skill provides the workflow; the custom agent and router select the worker model. Do not claim routing is verified from these instructions or a worker's self-report.

Supported orchestration workflow

There is no user-selectable mode switch. Thin-root orchestration is the only supported delegated workflow. “Thin” distinguishes this design from older, more Astra-active revisions; it is not one option in a mode menu.

Keep Astra focused on decisions where its judgment has the highest leverage: architecture, acceptance criteria, material risk, and final acceptance. After the contract is ready, Flash owns repository discovery needed within the brief, implementation, testing, debugging, and routine browser/visual QA.

For a normal phase, target this root workflow: one planning batch, one dispatch to one worker, one native wait, one batched acceptance review, and one final response. This is a workflow shape, not a reason to skip evidence or stop before the result is correct. Add Astra work only for a concrete blocker or architecture, security, or production-risk concern. Do not create root activity merely to observe progress.

1. Orient and classify

Read the relevant repository guidance and current request. Preserve existing work. Decide whether this is a direct small fix, a bounded build, or a large multi-phase project. Keep trivial edits with Astra; do not force delegation onto a typo or a simple question. For a substantial build, say what Astra will own and what Flash will implement. These routing decisions are not alternate modes.

Use the user's existing approvals and decisions. An explicit request to plan and build authorizes the in-scope workflow; it is not necessary to ask again after every phase. Ask only about material unresolved product/risk decisions that cannot be established from the repo. When asked to plan only, do not implement.

2. Confirm routing before delegating

Read routing.json in this installed skill and references/routing.md. Run the read-only scripts/doctor.py with the same CODEX_HOME/profile used by the session. The doctor reuses the installed route binding unless an explicit reviewed route is supplied. Never infer a provider from whichever catalog entry happens to exist. Its output is a static configuration check, not an end-to-end model test. Confirm the current ROOT is the user's selected GPT-6 Astra and that the native astra_flash_builder role is available. Do not change the root model or effort. Inspect project/CLI/UI/managed overrides that the doctor cannot resolve.

For an already-verified setup, reuse the verified configuration evidence rather than repeating a paid smoke test per task. For a first routed task, use a small real, useful implementation bundle and inspect host/router metadata afterward. No fake model-name check and no silent fallback. Do not run subagents certify, test-model --live, a smoke test or another paid probe as package setup. If routing cannot be established, finish the plan and report the execution blocker before delegating private work.

3. Design before dividing the work

Read references/planning.md. Reuse an approved design/spec and phase plan when one exists. Superpowers, GSD, and custom plans are all valid inputs; do not create a competing spec or force a framework migration.

For new work, produce an appropriately sized design covering objective, non-goals, repo evidence, important alternatives, interfaces, failure behavior, risks, and acceptance criteria. Put stable shared contracts ahead of dependent implementation. Astra makes architecture, auth/security, tenancy, payments, secrets, and production-impacting decisions; do not hand those decisions to Flash under a vague "build it" prompt.

4. Produce a phase plan and executable briefs

Default artifact home: docs/agent-work/<feature>/. Follow an existing project convention instead when one is established. Use the templates as needed.

Write a dependency-ordered phase plan. Prefer one coherent end-to-end vertical bundle per phase when its contract is stable. Each Flash assignment has exact contracts, a bounded file scope, testable outcomes, and verification commands. Internal discovery, implementation, testing, debugging, and routine UI validation stay with Flash; Astra does not write the entire implementation in the plan. Long work is welcome inside a clear contract, not across unknown architecture boundaries.

Separate tasks only at genuine dependency or independent acceptance boundaries, not for status visibility, every function, or every five-minute step. A phase can contain multiple bundles when required; a bundle can contain multiple test/code/fix cycles. Complete early phases first and refine later briefs when earlier interfaces stabilize. If a machine-readable plan is useful, use templates/plan.json and run scripts/validate_plan.py <plan.json> --repo-root <repository-root> before launch. That linter checks structure, not the truth or quality of the design.

5. Dispatch and let the worker work

Read references/execution.md. Use the host's actual native delegation tool with the installed astra_flash_builder role. Do not invent a slash command or tool signature. If the tool exposes explicit model selection, use the exact installed worker slug. Do not use a default explorer/reviewer role that could override it.

Give the worker the full task brief plus the minimum shared context, required contracts, task ID, working directory, baseline, and relevant dependency outputs. Prefer a clean child context where the host supports it; never claim its context is empty if the host actually inherits history. No unnecessary full-transcript forking, duplicate repository investigation, or play-by-play log forwarding.

Default to ONE active Flash writer in the current workspace. A native subagent is not automatically a Git worktree or a security sandbox. Astra must not edit its claimed paths. Use two writers only when the plan explicitly identifies independent work and each has a real, verified separate workspace. See execution reference for dirty-tree, contract, and integration rules. No recursive agents.

Let the worker complete its internal implementation, testing, debugging, and routine UI-validation loop. Use the longest practical native wait and allow it to finish. Do not poll with status/list calls, request progress updates, interrupt a healthy run, or perform overlapping repository work while the worker owns the bundle. A wait timeout alone is not evidence that the worker is stuck. Set a task-appropriate budget and checkpoint rule rather than assuming every job fits a fixed time estimate. Resume the same worker for one consolidated review-fix cycle; start a fresh context only for an unrelated bundle.

6. Review the actual result

Read references/review.md. Worker completion means ready for review, not accepted. Astra reviews the actual patch against the captured baseline, including untracked additions and pre-existing changes. Never treat HEAD as the baseline without checking the workspace state.

Apply two review lenses in one batched Astra pass: specification compliance, then code quality/security. Inspect the actual diff and the worker's evidence. Perform targeted spot checks where evidence is missing, a failure is plausible, or the risk justifies independent confirmation. Do not routinely rerun the worker's complete test suite or repeat visual QA that has adequate artifacts. Default reviewer subagents would also route to Flash, so do not mistake a default child for an independent Astra review.

Accept only after both lenses and relevant verification succeed. If corrections are needed, return all precise file-level findings to the same worker in one request. Default to at most one correction cycle, then accept or explicitly reassess the scope and risk. A second cycle is reserved for a material unresolved issue in the high-assurance exception below. Never silently switch model/provider.

7. Integrate, checkpoint, and finish

Astra integrates accepted dependencies in order, with the user's existing Git permissions. Do not auto-commit, merge branches, push, deploy, or apply production migrations just because a phase finished. Shared-workspace edits already exist in the workspace; don't invent a branch merge. Separate-worktree integration needs an explicit, reviewed operation and a valid common baseline.

Run cross-task checks once at a genuine integration boundary, and only when those checks add coverage beyond the worker's accepted evidence. Update the plan and a compact CHECKPOINT.md at meaningful resumable boundaries, not after every turn: completed/accepted work, current diff/workspace, contract decisions, pending tasks, worker thread IDs, review status, exact resume action. Preserve evidence before context compaction. Do not repeatedly reload full logs.

Conclude with what was built, what actually passed, outstanding limits, and the verified or unverified routing state. Never estimate cost savings from task counts or durations. Use actual provider usage records when measuring costs.

High-assurance exception

Expand Astra's investigation, checks, or correction cycle only when evidence shows material risk involving architecture, authentication/authorization, payments, tenancy, secrets, destructive migrations, production behavior, or high-impact shared infrastructure. State the reason for expanding the root loop. High assurance does not justify progress polling, duplicate implementation, or ritual reruns.

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 Astra Flash Orchestrator AI skill do?

Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.

Why use Astra Flash Orchestrator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ethanplusai/codex-on-crack/tree/main/legacy/astra-flash-orchestrator/skill/astra-flash-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 Astra Flash 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 Astra Flash Orchestrator?

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

Is the Astra Flash Orchestrator AI skill free?

Yes. It is published on GitHub by ethanplusai 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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