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 bearlike/Grove --skill using-grovegit clone --depth 1 https://github.com/bearlike/GroveWrote 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/bearlike/grove/using-grove)<a href="https://agentmods.dev/skills/bearlike/grove/using-grove"><img src="https://agentmods.dev/badge/skills/bearlike/grove/using-grove/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/bearlike/grove/using-grove"><img src="https://agentmods.dev/badge/skills/bearlike/grove/using-grove.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00092 | $0.04933 |
| Opus 5.5 | $0.00037 | $0.01973 |
| Sonnet 5.5 | $0.00018 | $0.00987 |
| Haiku 4.5 | $0.00009 | $0.00493 |
Grade A, and why
using-grove 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 18d 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 — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Grove
Grove runs a fleet of coding agents that live outside your context rather than sub-agents inside it. Each workspace is a whole separate agent process with its own runtime, its own context window and its own git worktree, addressable from outside. You create, steer, watch, pause, resume, respawn and kill them.
Reach for Grove when you want parallel workspaces that outlive a single turn. For quick in-session parallelism across a few scoped sub-tasks, plain sub-agents are lighter and fit better.
Grove earns its keep on work too big, too parallel or too cross cutting for one agent in one context.
- Decompose and parallelize. Split by component and spin up one workspace per slice. Each gets a clean worktree, so they never touch each other's files.
- Triage at scale. Forty Sentry issues or a whole backlog, one workspace per issue or per cluster. Each agent investigates independently and you review results rather than noise.
- Work one ticket from several angles at once. Implementation, tests and docs run in parallel in separate environments instead of one after another.
- Independent review. Point separate agents, on different models and different harnesses, at the same diff for uncorrelated second opinions.
The model
- One workspace is one isolated runtime for one agent. A dedicated git worktree, or the repo root for a lightweight case, plus a tmux session running that agent's own CLI, on the host or inside a container.
- Model and harness agnostic by design. Every workspace picks its own agent and model. The point is giving each slice the harness that suits it, not stretching one agent across everything.
- You own the fleet and each member owns its own task. Decide the split, spawn one workspace per slice, then poll and steer and collect. A map step and a reduce step, with real isolation so one agent's long tool call never blocks another.
What Grove reports back
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.
- 18d ago Changed · +3 lines 93def194e7af
- 26d ago Changed · +6 lines 33e38bbe068c
- 28d ago Changed · -3 lines · -4 tokens per session fd201e63310d
- 1mo ago Changed · +51 lines · +96 tokens per session 66c2969127f5
- 1mo ago First seen · 312 lines · 0 tokens per session scan A 4fa7551c41bd
using-grove is a skill published in the GitHub repository bearlike/Grove (9 stars, last pushed 18d ago), licensed MIT. It adds 92 tokens to every session and 4,933 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 2026-08-31.
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