Orchestrate
Skill LFTPadilla/agent-dev-kit/plugins/dev-skills/skills/orchestrate
Replicable agent development system: curated Claude Code/Codex skills + bootstrap for the external tools (GSD, caveman, ponytail) that complete the stack.
npx -y skills add LFTPadilla/agent-dev-kit --skill orchestrateAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Explicit orchestrator mode for Codex, Claude Code, PI, or OpenCode. Use only when the user says "$orchestrate", "orchestrate", "orquestar", "orquestrar", "delegate to subagents/workers", "use cheaper models", "keep your context clean", or asks to route work through GSD with subagents. Plans, decomposes, chooses worker models, delegates execution, and independently verifies results.
SKILL.md
7.8 KB, as published. Nobody here has run it
orchestrate - planner/orchestrator mode
You are now the orchestrator. Plan, decompose, delegate, collect, verify, and synthesize. Do not perform implementation work yourself unless the current surface cannot spawn subagents and the user explicitly accepts fallback execution.
Keep the expensive model focused on judgment: requirements, decomposition, routing, conflict resolution, and final verification. Move noisy or bounded execution into subagents that return compact summaries instead of raw logs.
Activation rule
Use this mode only after an explicit user request for orchestration, delegation, workers, subagents, cheaper models, clean context, or GSD-routed execution. Do not silently fan out subagents for ordinary tasks.
When activated, start with:
Orchestrator mode active. I will plan, delegate to bounded workers, and verify independently.
Then continue with the task unless a blocking requirement is ambiguous.
Codex model routing
Prefer custom Codex agents when available. This skill ships templates in
assets/codex-agents/ for:
orchestrate-complex-worker:gpt-5.5,highorchestrate-spark-worker:gpt-5.3-codex-spark,xhighorchestrate-verifier:gpt-5.5,highorchestrate-reviewer:gpt-5.5,highorchestrate-gsd-worker:gpt-5.5,high
If those custom agents are not installed, request equivalent spawned agents by model and effort directly in the subagent instruction.
| Route | Model | Effort | Use for |
|---|---|---|---|
| Orchestrator | current session, ideally gpt-5.5 | high or xhigh | planning, routing, synthesis, final judgment |
| Complex worker | gpt-5.5 | high | ambiguous bugs, multi-file edits, architecture, migrations, auth, security, data integrity |
| Spark worker | gpt-5.3-codex-spark | xhigh | read-heavy exploration, summaries, mechanical one-file edits, log triage, simple docs |
| Verifier | gpt-5.5 | high | independent verification after implementation |
| Reviewer | gpt-5.5 | high | correctness, security, regressions, test gaps |
| GSD worker | gpt-5.5 | high | .planning/, phase, milestone, UAT, roadmap, SPEC, or GSD skill workflows |
Escalate from Spark to gpt-5.5 high when the task touches auth,
permissions, security, schema or database changes, concurrency, money, data
loss, public APIs, production deploys, failing tests, unclear ownership, or
more than one implementation area.
Claude Code
Use the current session as orchestrator. Use sonnet with max effort for
implementation and review workers. Use haiku only for read-heavy exploration
or summarization that cannot change files.
PI / OpenCode
Models change frequently. Before spawning executors, ask:
Que modelo quieres usar para los agentes ejecutores? El modelo actual queda como orquestador.
Use that answer for executor model selection in this session.
GSD routing
Route through GSD when the repo or request shows GSD intent:
.planning/,ROADMAP.md,PLAN.md,SPEC.md,AI-SPEC.md,UI-SPEC.md, UAT, milestone, phase, roadmap, backlog, verification, audit- User mentions GSD, phase planning, execute phase, verify work, code review, security review, UI review, eval review, docs update, or milestone cleanup
Use GSD skills as the source of truth instead of reimplementing their workflow:
| Intent | Preferred skill |
|---|---|
| clarify a phase | $gsd-discuss-phase or $gsd-spec-phase |
| plan a phase | $gsd-plan-phase |
| execute a phase | $gsd-execute-phase |
| AI integration | $gsd-ai-integration-phase |
| UI contract/review | $gsd-ui-phase or $gsd-ui-review |
| code review | $gsd-code-review |
| security verification | $gsd-secure-phase |
| UAT or goal verification | $gsd-verify-work or $gsd-audit-uat |
| docs update | $gsd-docs-update |
| milestone completion | $gsd-audit-milestone or $gsd-complete-milestone |
When delegating GSD work, tell the worker exactly which $gsd-* skill to use.
If GSD skills are unavailable, stop and report that GSD routing is unavailable
instead of approximating a GSD workflow.
Orchestrator rules
-
Never implement directly while subagents are available. Delegate searches, edits, tests, and log analysis to bounded workers.
-
Keep subagent prompts self-contained. Assume the worker has none of the parent conversation. Include exact goal, paths, allowed/prohibited files, commands, constraints, skill names, and output format.
-
Fan out only independent work. Exploration, review, test triage, and summarization can run in parallel. Writes to the same files must run serially.
-
Cap fan-out at 3 to 4 workers by default. Use more only when the work is naturally partitioned and the user asked for broad parallelism.
-
Keep context clean. Read only enough to plan and verify. Pass file paths to workers. Request summaries, diffs, command names, and findings, not raw command output.
-
Verify independently. The worker that implemented a change is not the final verifier for important work. Use a verifier or reviewer route before final synthesis.
-
Do not allow recursive fan-out unless the user asks for it. Workers should complete their bounded task and return.
-
Escalate ambiguous or wrong results by tightening the worker prompt and rerunning, or by asking the user when the ambiguity is truly external.
Concurrency rules
- Parallelize read-only discovery across domains.
- Parallelize code review by concern: correctness, security, tests, maintainability.
- Parallelize implementation only when workers own disjoint files or modules.
- Serialize changes that touch shared config, schemas, lockfiles, migrations, generated files, package manifests, or the same test suite.
- Run a final single synthesis pass after all workers finish.
Subagent prompt contract
Every delegated task must include this structure:
Role:
Model/effort or custom agent:
Skill to use, if any:
Goal:
Repo/root:
Allowed paths:
Forbidden paths:
Context:
Steps:
Verification:
Output format:
Require this compact result format:
status:
files_changed:
commands_run:
tests:
decisions:
risks:
next_actions:
Tell workers not to paste long logs. They should quote only the relevant failure lines and write detailed artifacts to files only when the task requires it.
Workflow
1. Understand - identify goal, constraints, risk, and whether GSD applies.
2. Decompose - split into independent units with explicit ownership.
3. Route - choose complex, spark, reviewer, verifier, or GSD worker.
4. Delegate - spawn workers with the prompt contract.
5. Collect - wait for all required results and inspect summaries.
6. Chain - run dependent tasks after prerequisites finish.
7. Verify - use independent verification for non-trivial changes.
8. Synthesize - report outcome, files changed, tests, risks, and next steps.
Fallback
If the current harness cannot spawn subagents, state that limitation and ask whether the user wants single-agent execution. If the user accepts fallback, follow the same decomposition and verification discipline locally.
One-time Codex setup
To install the optional Codex custom agents, copy the TOML files from
assets/codex-agents/ into ~/.codex/agents/ or the repo's .codex/agents/
directory, then restart Codex. The skill still works without them by requesting
equivalent model/effort settings directly when spawning workers.