astra-orchestrator
Skill1 starsChanged 40 days agoMITView on GitHub
Orchestrate complex Codex coding work for the Pro profile with GPT-6 Astra at medium reasoning as planner/integrator, Luna subagents for exploration, implementation, testing, and research, and an Astra reviewer. Use for multi-file features, debugging across components, repo-wide changes, parallelizable workstreams, or whenever the user asks to delegate or use subagents. Do not use for trivial one-file edits or simple questions.
In this repository21 other files it gives its agents.
- .cursor/rules/global.mdcCursor rule
- .cursor/rules/kata-code-browser.mdcCursor rule
- .cursor/rules/lifecycle.mdcCursor rule
- .cursor/rules/open-pstack-models.mdcCursor rule
- .cursor/rules/ponytail.mdcCursor rule
- .cursor/rules/sub-agents.mdcCursor rule
- 1passwordSkill
- eli5Skill
- readmeSkill
- rebase-kata-upstreamSkill
- release-testflightSkill
- resolve-reviewsSkill
- show-meSkill
- test-t3-appSkill
- test-t3-mobileSkill
- thermo-nuclear-code-quality-reviewSkill
- thermo-nuclear-reviewSkill
- thermo-runSkill
- thermosSkill
- unslopSkill
- verify-katacodeSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
52 rules. About 0 are common, 46 are distinctive, and 6 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈46
Distinctive · estimate
This author's own choices. Worth a look.
6
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Use the root agent as the high-quality orchestrator.
- Distinctive: line 25
Use Luna for all routine subagent execution.
- Distinctive: line 31
Do not override a Luna subagent to a more expensive model unless the user explicitly asks for escalation or a Luna worker reports that the …
- Distinctive: line 37
Before doing substantive repository work, classify the task as either:
- Distinctive: line 42
Use root-only only when the task is genuinely small, localized, and does not materially benefit from independent exploration, …
- Very specific: line 56
When a task qualifies for delegation, the root MUST call spawn_agent before performing the delegated work itself.
- Distinctive: line 58
Do not merely describe, simulate, or internally reason about delegation.
- Very specific: line 62
If spawn_agent is unavailable or fails, explicitly report that failure.
- Distinctive: line 64
Do not silently fall back to doing required delegated work in the root thread.
- Distinctive: line 68
Do not create subagents solely to satisfy this rule when the task is genuinely root-only.
- Distinctive: line 98
When spawning agents, use these models by default:
- Distinctive: line 115
wait for required agents before final synthesis
- Distinctive: line 117
Do not silently substitute the root agent for a required Luna worker.
- Very specific: line 119
Do not spawn Astra workers except for the reviewer role unless:
- Distinctive: line 131
Every delegated task should include:
- Distinctive: line 140
Prefer narrow tasks that can finish independently.
- Distinctive: line 205
Run independent tasks in parallel.
- Distinctive: line 207
When two or more delegated tasks are independent, spawn all of them before waiting for any one of them.
- Distinctive: line 214
wait for all three
- Distinctive: line 217
Do not do this:
- Distinctive: line 244
Do not send multiple workers to edit the same files unless the root explicitly coordinates ownership.
- Distinctive: line 246
Prefer one writer per file or subsystem.
- Distinctive: line 255
wait for exploration results
- Distinctive: line 266
Do not spawn every role mechanically.
- Distinctive: line 268
Use only the roles that materially improve the task.
- Distinctive: line 284
use Astra reviewer for high-risk or non-obvious fixes
- Distinctive: line 286
Do not let multiple workers independently attempt competing fixes unless the root intentionally requests alternative approaches.
- Distinctive: line 292
When current or version-specific external information matters:
- Distinctive: line 299
Do not mix speculative external claims into implementation decisions without verification.
- Distinctive: line 305
Use Luna for routine subagent execution.
- Distinctive: line 307
Keep the root context focused on:
- Distinctive: line 316
Do not paste large raw logs or entire files back into the root when a concise evidence summary is enough.
- Distinctive: line 327
Avoid returning large amounts of irrelevant raw output.
- Distinctive: line 354
If a subagent fails:
- Distinctive: line 358
do not silently ignore the failed delegation
- Distinctive: line 359
do not claim the delegated work completed successfully
- Very specific: line 361
If spawn_agent itself fails, explicitly note the failure.
- Distinctive: line 363
If a required worker fails repeatedly, the root may continue directly when reasonable, but should record that the fallback occurred.
- Distinctive: line 369
Before producing the final answer for a delegated task, confirm that:
- Distinctive: line 371
every required subagent was actually spawned
- Distinctive: line 372
every required subagent either completed or explicitly failed
- Distinctive: line 376
no required agent is still running
- Distinctive: line 378
Do not finish while required subagents are still running.
- Very specific: line 380
Do not claim delegation occurred unless spawn_agent was actually called successfully.
- Distinctive: line 386
Before claiming completion, the root should:
- Distinctive: line 390
check material reviewer findings
- Distinctive: line 391
run or confirm the highest-value tests
- Distinctive: line 392
verify that delegated results were integrated correctly
- Distinctive: line 408
Do not narrate every subagent action unless the user asks for detailed orchestration visibility.
- Distinctive: line 417
When useful, briefly mention which agents contributed.
- Distinctive: line 419
If the user explicitly asks to see delegation, report:
- Very specific: line 426
Do not claim a Luna agent was used unless the trace contains a successful spawn_agent call using gpt-5.6-luna.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Astra Orchestrator — Pro Profile
The user's explicit instructions take precedence over this skill.
Goal
Use the root agent as the high-quality orchestrator.
Delegate bounded execution work to specialized subagents, then have the root integrate, verify, and present the final result.
The expected default topology is:
- root: GPT-6 Astra at medium reasoning
- explorer: GPT-5.6 Luna at max reasoning
- worker: GPT-5.6 Luna at max reasoning
- tester: GPT-5.6 Luna at max reasoning
- reviewer: GPT-6 Astra at low reasoning
- researcher: GPT-5.6 Luna at max reasoning
Use Luna for all routine subagent execution.
This is a requirement, not a preference.
The root and reviewer use Astra; routine execution subagents use Luna.
Do not override a Luna subagent to a more expensive model unless the user explicitly asks for escalation or a Luna worker reports that the task requires higher-level reasoning.
Delegation gate
Before doing substantive repository work, classify the task as either:
- root-only
- delegated
Use root-only only when the task is genuinely small, localized, and does not materially benefit from independent exploration, implementation, testing, research, or review.
The task MUST be delegated when at least one of the following is true:
- the task spans multiple files, modules, services, or components
- there are two or more independent workstreams
- repository exploration is needed before implementation
- implementation and verification benefit from separate context
- debugging requires tracing across components
- multiple modules or services need inspection
- external or version-specific facts need verification
- an independent post-change review is materially useful
- the user explicitly asks for delegation, parallelism, agents, or subagents
When a task qualifies for delegation, the root MUST call spawn_agent before performing the delegated work itself.
Do not merely describe, simulate, or internally reason about delegation.
Actual subagents must be spawned.
If spawn_agent is unavailable or fails, explicitly report that failure.
Do not silently fall back to doing required delegated work in the root thread.
For every delegated task, spawn at least one subagent.
Do not create subagents solely to satisfy this rule when the task is genuinely root-only.
Root-agent responsibilities
The root agent owns:
- understanding the user's actual goal
- choosing the architecture and implementation direction
- decomposing the task
- deciding which tasks can run in parallel
- spawning the appropriate subagents
- giving each subagent a bounded contract
- resolving conflicting subagent findings
- integrating changes
- reviewing the final diff
- running or coordinating final verification
- presenting the final result to the user
Subagents provide evidence and bounded execution.
They do not own the overall direction.
The root must not offload architectural ownership to a subagent.
Spawn policy
When spawning agents, use these models by default:
- explorer:
gpt-5.6-lunaatmaxreasoning - worker:
gpt-5.6-lunaatmaxreasoning - tester:
gpt-5.6-lunaatmaxreasoning - researcher:
gpt-5.6-lunaatmaxreasoning - reviewer:
gpt-6-astraatlowreasoning
The root keeps the Pro profile configuration from .codex/config.toml: GPT-6 Astra at medium reasoning. The role files in .codex/agents/ explicitly set Luna reasoning to max and reviewer reasoning to low. Preserve those efforts when spawning agents unless the user requests a change. Do not change the root model from within a session.
For every delegated task:
- call
spawn_agent - give the agent a descriptive task name using underscores
- explicitly specify the intended model
- give the subagent a bounded delegation contract
- retain the returned task name or identifier
- wait for required agents before final synthesis
Do not silently substitute the root agent for a required Luna worker.
Do not spawn Astra workers except for the reviewer role unless:
- the user explicitly requests Astra
- Luna reports a genuinely difficult reasoning blocker
- the root determines that a high-risk architectural or security review needs Astra
Routine execution should remain on Luna.
Delegation contract
Every delegated task should include:
- Objective: one concrete outcome
- Scope: exact files, module, subsystem, or question when known
- Context: only the information needed to succeed
- Constraints: what must not change
- Deliverable: what the subagent must return or implement
- Acceptance criteria: how success will be checked
Prefer narrow tasks that can finish independently.
Bad:
Fix the backend.
Good:
Trace where POST /invoices validates currency. Return the responsible files, validation path, and existing tests. Do not edit files.
For implementation tasks, explicitly state file ownership when possible.
For exploration tasks, tell the agent not to edit files.
For review tasks, tell the agent to report findings rather than silently modify unrelated code.
Role selection
Use explorer for:
- repository mapping
- tracing execution or data flow
- locating symbols and tests
- dependency inspection
- configuration inspection
- identifying implementation boundaries
Use worker for:
- bounded implementation
- small refactors with explicit scope
- targeted fixes
- adding requested code
- modifying clearly owned files
Use tester for:
- reproduction
- targeted test execution
- validation
- regression checks
- adding tests when requested or clearly required by the task
Use reviewer for:
- independent post-change review
- correctness checks
- security review
- regression analysis
- missing-test analysis
- architectural consistency checks
Use researcher for:
- current API or framework behavior
- dependency or version questions
- primary documentation verification
- external compatibility questions
Parallelism
Run independent tasks in parallel.
When two or more delegated tasks are independent, spawn all of them before waiting for any one of them.
Good parallel set:
- spawn backend explorer
- spawn frontend explorer
- spawn API researcher
- wait for all three
- synthesize findings
Do not do this:
- spawn backend explorer
- wait
- spawn frontend explorer
- wait
- spawn researcher
- wait
unless later tasks genuinely depend on earlier results.
Good parallel examples:
- explorer maps backend path
- explorer maps frontend path
- researcher verifies external API behavior
Serialize dependent work:
- explore
- decide architecture
- implement
- test
- review
- fix material findings
- final verification
Do not send multiple workers to edit the same files unless the root explicitly coordinates ownership.
Prefer one writer per file or subsystem.
Default coding workflow
For non-trivial implementation tasks, prefer this sequence:
- spawn one or more Luna explorers if repository understanding is needed
- wait for exploration results
- root decides implementation direction
- spawn Luna worker or workers with bounded ownership
- wait for implementation
- spawn Luna tester
- wait for validation
- spawn Astra reviewer when an independent review is materially useful
- resolve material findings
- run final verification
- present the result
Do not spawn every role mechanically.
Use only the roles that materially improve the task.
However, once the delegation gate is satisfied, at least one real subagent must be spawned.
Debugging workflow
For cross-component bugs:
- spawn explorers for independent suspected areas
- reproduce the issue when possible
- collect evidence before selecting a fix
- root determines the likely root cause
- assign a bounded Luna worker to implement the fix
- assign Luna tester to reproduce the original failure and validate the fix
- use Astra reviewer for high-risk or non-obvious fixes
Do not let multiple workers independently attempt competing fixes unless the root intentionally requests alternative approaches.
Research workflow
When current or version-specific external information matters:
- spawn a Luna researcher
- require primary or authoritative sources when possible
- return concise findings and compatibility implications
- let the root decide how those findings affect implementation
Do not mix speculative external claims into implementation decisions without verification.
Cost and context discipline
Use Luna for routine subagent execution.
Keep the root context focused on:
- architectural decisions
- summarized evidence
- important diffs
- test results
- reviewer findings
- unresolved risks
Do not paste large raw logs or entire files back into the root when a concise evidence summary is enough.
Subagents should return:
- conclusions
- relevant file paths
- important line or symbol references
- commands run
- test results
- risks or blockers
Avoid returning large amounts of irrelevant raw output.
Escalation behavior
A subagent should report back instead of expanding scope when it encounters:
- an architectural decision
- a breaking API or schema change
- a new dependency
- a security-sensitive design choice
- unclear requirements with materially different outcomes
- unexpected changes outside its assigned scope
- changes that affect another worker's ownership
- a blocker that requires substantially broader reasoning
The root decides what to do next.
Luna should not independently escalate itself to a more expensive model.
The root owns model escalation decisions.
Failure handling
If a subagent fails:
- inspect the failure reason
- decide whether the task should be retried, narrowed, reassigned, or handled by the root
- do not silently ignore the failed delegation
- do not claim the delegated work completed successfully
If spawn_agent itself fails, explicitly note the failure.
If a required worker fails repeatedly, the root may continue directly when reasonable, but should record that the fallback occurred.
Delegated-task completion gate
Before producing the final answer for a delegated task, confirm that:
- every required subagent was actually spawned
- every required subagent either completed or explicitly failed
- material findings were integrated
- conflicting findings were resolved
- required verification was performed
- no required agent is still running
Do not finish while required subagents are still running.
Do not claim delegation occurred unless spawn_agent was actually called successfully.
Final verification
Before claiming completion, the root should:
- inspect the final diff
- confirm the requested behavior is actually implemented
- check material reviewer findings
- run or confirm the highest-value tests
- verify that delegated results were integrated correctly
- state any validation that could not be performed
For implementation tasks, prefer checking:
- syntax or type checks
- targeted unit tests
- integration tests where relevant
- build success where relevant
- the original reproduction path
- final diff for unintended changes
User-facing behavior
Do not narrate every subagent action unless the user asks for detailed orchestration visibility.
The final answer should focus on:
- what changed
- what was verified
- important findings
- remaining risks or limitations
When useful, briefly mention which agents contributed.
If the user explicitly asks to see delegation, report:
- subagent name
- model
- assigned task
- completion status
Do not claim a Luna agent was used unless the trace contains a successful spawn_agent call using gpt-5.6-luna.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

