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/drn/dots/orchestratenpx skills add drn/dots --skill orchestrategit clone --depth 1 https://github.com/drn/dotsWhat 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 | $0.00088 | $0.02711 |
| Opus 5 | $0.00044 | $0.01355 |
| Sonnet 5 | $0.00018 | $0.00542 |
| Haiku 4.5 | $0.00009 | $0.00271 |
Grade A, and why
orchestrate 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 2d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate: Tiered Planning + Implementation
Run the expensive top-tier session model only where it earns its cost — planning, decomposition, orchestration, and final integration — and fan implementation out through the Workflow tool to model-tiered subagents: Sonnet for routine work, Opus for complex work.
The top-tier planner/orchestrator is the session model — Fable when it is available, Opus when Fable is not. Planning runs in the main loop, so it automatically uses whichever model the session is on; no detection step is needed.
Invoking this skill is the explicit opt-in the Workflow tool requires.
Arguments
$ARGUMENTS— required: description of the feature or change to build, or a path to a plan/spec file.
If no arguments are provided, ask the user what to build and stop.
Context
- Current branch: !
git branch --show-current 2>/dev/null | head -1 - Git status: !
git status --short 2>/dev/null | head -20 - Project type: !
find . -maxdepth 1 \( -name go.mod -o -name package.json -o -name Cargo.toml -o -name pyproject.toml -o -name Gemfile -o -name Makefile \) 2>/dev/null | head -6 - Recent commits: !
git log --oneline -5 2>/dev/null | head -5
Step 1: Plan in the Main Loop
You (the session model) are the planner and orchestrator. Do NOT delegate planning to a cheaper model, and do NOT implement the tasks yourself.
- Understand the task. Explore the codebase and read the files the change will touch. For large or unfamiliar codebases, fan out scouting to Explore subagents first.
- Decompose into work items. Each work item needs:
- id — short slug, e.g.
api-endpoint - prompt — fully self-contained: file paths, existing conventions to follow, exact success criteria, and the instruction to run a sanity check (compile/lint) before finishing. Subagents have no conversation history; the prompt is everything they know.
- tier —
sonnetoropus(rubric below) - files — the files this item owns
- id — short slug, e.g.
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.
- 2d ago First seen · 164 lines · 88 tokens per session scan A cdfdb31abd0d
orchestrate is a skill published in the GitHub repository drn/dots (23 stars, last pushed 4d ago), licensed MIT. It adds 88 tokens to every session and 2,711 once invoked, about $0.0004 per session on Opus 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.
Other skills, from other repositories
paperclip
Interact with the Paperclip control plane API for task coordination and governance. Use when checking assignments, updating issue status, posting comments, delegating work, managing routines, or calling Paperclip API endpoints.
omni-mcp
Connect to the OmniRoute MCP server (110 tools, 3 transports: SSE/stdio/HTTP). Covers routing, cache, compression, memory, skills, providers, and audit tools across 33 permission scopes.
add-resend
Add Resend (email) channel integration via Chat SDK.
codegen
Code generation utilities for json-render. Use when generating code from UI specs, building custom code exporters, traversing specs, or serializing props for @json-render/codegen.
agui-dotnet-sample-step
Add a GettingStarted sample Step (a Server/Client pair) to the AG-UI .NET SDK that demonstrates one protocol feature the way we want users to write it. USE FOR: adding a new samples/GettingStarted/StepNN Server+Client pair, wiring it into AGUI.slnx and the integration-test project, giving it a deterministic…
optimize-agentic-workflow
Analyze and reduce token consumption in agentic workflows — guardrail-specific entry points, measurement, and optimization techniques.