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 skills add ZinkLu/Orca-Orchestration --skill skillgit clone --depth 1 https://github.com/ZinkLu/Orca-OrchestrationWrote 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/zinklu/orca-orchestration/skill)<a href="https://agentmods.dev/skills/zinklu/orca-orchestration/skill"><img src="https://agentmods.dev/badge/skills/zinklu/orca-orchestration/skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zinklu/orca-orchestration/skill"><img src="https://agentmods.dev/badge/skills/zinklu/orca-orchestration/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00092 | $0.02017 |
| Opus 5.5 | $0.00037 | $0.00807 |
| Sonnet 5.5 | $0.00018 | $0.00403 |
| Haiku 4.5 | $0.00009 | $0.00202 |
Grade A, and why
orca-dag scanned grade A with 0 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
orca-dag
Refine a software requirement through conversation and land it as an Orca orchestration task DAG (directed acyclic graph). You own building and reshaping the graph; execution is triggered by the user in the orca-dag viewer — the viewer advances the whole graph automatically, in parallel, along its dependencies. It is not fired node by node.
Every planning result must be written into Orca's orchestration state (by running orca orchestration commands), not left as chat text — the viewer polls Orca live and draws whatever is there.
Tools you may use
- Bash to run the
orcaCLI and read/write orchestration state. - Read / Write / Edit to write planning docs in the working directory (
docs/PRD.md,docs/TECH_SPEC.md). - Do not run destructive commands unrelated to this task (
rm,git push, deleting files, …).
Preflight
orca status --json # runtime.state should be "ready"; if not, ask the user to run `orca open` first
# runtime.appVersion must be >= 1.4.160 (the Run/Dispatch contract)
Workflow (three phases, all in conversation)
- Requirement clarification (PRD): align on the goal, MVP scope, and explicit non-goals with short questions — one key question at a time. If MVP is enough, plan only P0; don't over-design. Once agreed, write
docs/PRD.md. - Technical design (TECH_SPEC): stack, data model (down to fields), module interfaces (pseudocode). Write
docs/TECH_SPEC.md. - Decompose into a task DAG: split the design into parallel/serial subtasks and create the tasks and dependencies with the commands below. This step is the required output.
Writing the DAG into Orca (the core)
Step 0: create a Run first (required on Orca ≥ 1.4.160)
Tasks are no longer global: every task belongs to a Run, and task-create / task-list fail with run_required when no Run is bound. So start every plan by opening a fresh Run to hold this DAG:
orca orchestration run-create --objective "<one sentence on what this plan does>" --json
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.
- 1mo ago First seen · 117 lines · 92 tokens per session scan A a228544b0371
orca-dag is a skill published in the GitHub repository ZinkLu/Orca-Orchestration (140 stars, last pushed 2mo ago), licensed MIT. It adds 92 tokens to every session and 2,017 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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