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/mvschwarz/openrig/openrig-herdrnpx skills add mvschwarz/openrig --skill openrig-herdrgit clone --depth 1 https://github.com/mvschwarz/openrigWrote 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/mvschwarz/openrig/openrig-herdr)<a href="https://agentmods.dev/skills/mvschwarz/openrig/openrig-herdr"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/openrig-herdr.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 | $0.00132 | $0.02028 |
| Opus 5 | $0.00066 | $0.01014 |
| Sonnet 5 | $0.00026 | $0.00406 |
| Haiku 4.5 | $0.00013 | $0.00203 |
Grade A, and why
openrig-herdr 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 5d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
openrig-herdr
OpenRig decides which agents make up a view (a rig, a pod, a mission, a slice, or a saved
group); herdr renders the pixels. A view opens as live, interactive terminal tiles — each tile
is a nested tmux attach to a daemon-owned agent session, so you get the real session, not a
snapshot. OpenRig owns the semantics; herdr owns the surface. You drive it entirely through the
rig terminal CLI, which rides the installed herdr binary at arm's length — never link, embed, or
plugin it.
The whole surface — three verbs
rig terminal open <view> [--provider herdr|cmux] [--json] # herdr is the default provider
rig terminal views [--json] # list openable views (saved + derived)
rig terminal status [--provider] [--json] # provider liveness / health
<view> resolves, in order, to one of:
- a rig name — every live agent in that rig, auto-laid-out;
pod:<rig>/<podNamespace>— every live agent in one pod of a rig (the rig's inventory filtered by pod);mission:<id>— the agents working that mission (derived live from topology);slice:<id>— the agents working that slice (derived live);- a saved-view name — a user-defined group (see Saved views).
Compose a view from a sentence
An agent asked "open all my rigs plus a mission as views" runs one open per target:
rig terminal open acme-web # a whole rig, live agents as tiles
rig terminal open mission:site-relaunch # exactly the agents working the mission
rig terminal open slice:search-filters # the agents working one slice
No hand-listing of seats: the mission/slice membership is derived from live topology at open time.
Read the result honestly — partial and degrade
The result is a partition — every seat lands in exactly one bucket, each named:
- opened — the live agents now showing as tiles.
- absent — seats in the view that aren't currently live: named and skipped, never silently dropped. A view that opens some of its seats is a success (the partial is disclosed) and exits 0. Only a view where no pane opens exits non-zero.
- degraded — an agent that structurally cannot tile, named with the reason. The v1 case:
an agent on a host registered over HTTP has no ssh path, so its tile can't be composed — it reads
as "host
<id>is http-registered; tiles need ssh" and is skipped, never dropped. (ssh-reachable hosts tile via an ssh-wrapped attach; full http-host tiling is deferred to the cross-host transport seam.)
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.
- 5d ago First seen · 154 lines · 132 tokens per session scan A 37508a02f50b
openrig-herdr is a skill published in the GitHub repository mvschwarz/openrig (65 stars, last pushed today), licensed Apache-2.0. It adds 132 tokens to every session and 2,028 once invoked, about $0.0007 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
agent-teams
Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
skill-router
The index of every pro-workflow skill and command, grouped by job, with when to reach for each and whether it is human-run or auto-triggered. Use when you are not sure which skill fits, want the full map, or ask "what can this do", "which skill for X", "list the workflow".
token-efficiency
Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient.
cost-tracker
Track session costs, set budget alerts, and optimize token spend. Use to check costs mid-session or set spending limits.
replay-learnings
Surface past learnings relevant to the current task before starting work. Searches correction history, recalls past mistakes, and applies prior patterns. Use when starting a task, saying "what do I know about", "previous mistakes", "lessons learned", or "remind me about".
wrap-up
End-of-session ritual that audits changes, runs quality checks, captures learnings, and produces a session summary. Use when saying "wrap up", "done for the day", "finish coding", or ending a coding session.