Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dimkurilo/opencode-skills/multi-model-orchestrationnpx skills add dimkurilo/opencode-skills --skill multi-model-orchestrationgit clone --depth 1 https://github.com/dimkurilo/opencode-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dimkurilo/opencode-skills/multi-model-orchestration)<a href="https://agentmods.dev/skills/dimkurilo/opencode-skills/multi-model-orchestration"><img src="https://agentmods.dev/badge/skills/dimkurilo/opencode-skills/multi-model-orchestration.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00151 | $0.06567 |
| Opus 5 | $0.00076 | $0.03284 |
| Sonnet 5 | $0.00030 | $0.01313 |
| Haiku 4.5 | $0.00015 | $0.00657 |
Grade C, and why
multi-model-orchestration scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
REVIEW_MODE: Mechanical | Simple | Ordinary | Strong <!-- orchestrator-set; reviewer does NOT compute post-factum; carries gate context (effort, lens, dispatch count) --> Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**Canonical examples:** `skills/wave-spec/SKILL.md` §Deploy Probe — curl dual-pattern + skills-repo check. How it starts
The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Model Orchestration
Coordinator dispatches tasks to 2+ model workers via Orca, waits for results, synthesizes. Coordinator dispatches, waits, synthesizes, gates. Writing code in a coordinator session requires an explicit owner-pin switch to writer role — coordinator does not combine orchestration with code writing in the same task. Coordinator = any model with this skill loaded. This skill does not prescribe coordinator family.
TL;DR (minimum path)
multi-model-orchestration = dispatch/review движок для ≥2 моделей. Координатор маршрутизирует задачи воркерам (Orca), ждёт, синтезирует. Координатор не совмещает оркестрацию с написанием кода в одной задаче.
Когда: «обсудите с 2 моделями», cross-validate, parallel review, fidelity port, security/RLS (никогда одной моделью). Когда нет: §1 говорит solo, тривиал, одна модель.
Ядро: solo-vs-multi дерево (§1) → роутинг моделей (§2) → cross-family rule: writer.family ≠ reviewer.family → brief (§4) → dispatch через Orca (dispatch --inject, НЕ terminal send) → wait (check --wait) → синтез (§7, stricter wins). Worker contract: SUMMARY/EVIDENCE/CHANGES/RISKS/BLOCKERS + written≠persisted.
Роутер: новый проект → project-bootstrap · план спринта → wave-spec · 2+ модели → multi-model-orchestration. Lifecycle gates (Commit/PR/Merge/Deploy/On prod) — канон в wave-spec.
0. Preconditions
orca status --json # runtime up?
If Orca unavailable → manual fallback (§0b). Do not guess flags.
Skills required: orca-cli (terminal ops), orchestration (task/dispatch/wait).
0b. Manual Fallback (Orca unavailable)
If orca status fails or Orca is not installed:
- Coordinator opens N terminal tabs/windows manually (one per model).
- In each terminal: launch the model's CLI (
opencode,codex, etc.). - Copy-paste the model-specific brief (from
references/routing.md) into each terminal. - Collect outputs manually when workers finish.
- Synthesize per §7.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 399 lines · 151 tokens per session scan C 1ad4d9188182
multi-model-orchestration is a skill published in the GitHub repository dimkurilo/opencode-skills (6 stars, last pushed 21d ago), licensed MIT. It adds 151 tokens to every session and 6,567 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 2 findings (hidden instructions, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…