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 Rockielab/rockie-claude --skill deploy-teamgit clone --depth 1 https://github.com/Rockielab/rockie-claudeWrote 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/rockielab/rockie-claude/deploy-team)<a href="https://agentmods.dev/skills/rockielab/rockie-claude/deploy-team"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/deploy-team/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/rockielab/rockie-claude/deploy-team"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/deploy-team.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 | $0.00116 | $0.00964 |
| Opus 5 | $0.00058 | $0.00482 |
| Sonnet 5 | $0.00023 | $0.00193 |
| Haiku 4.5 | $0.00012 | $0.00096 |
Grade A, and why
deploy-team 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deploy-team — 95% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/deploy-team — configurable agent teams via Claude CLI
Spawns N claude CLI processes in parallel, each with a distinct role prompt,
shared access to a thread, and (optionally) shared context files. Returns a
concatenated report the main agent reads and synthesizes.
When to use a team vs a single subagent
- Single subagent (via native Agent tool): focused, single-responsibility work with a known output shape. Example: "research agent: find 3 recent papers on X and summarize."
- Team (
/deploy-team): multi-perspective work where agents should see each other's findings and challenge them. Example: "gauntlet a new experiment idea — brainstorm, research, attack, validate."
Invocation
python3 .claude/skills/deploy-team/runtime/orchestrator.py \
--template gauntlet \
--topic "should we run the quadratic readout PC control next?" \
[--context matrix-thinking/KILL_LIST.md EXPERIMENT_LOG.md] \
[--max-minutes 15]
Or with a custom config instead of a template:
python3 .claude/skills/deploy-team/runtime/orchestrator.py \
--config path/to/custom-team.json \
--topic "..."
Templates
- gauntlet — 4 agents: brainstormer, researcher, attacker, validator. For: new ideas. The attacker tries to kill; validator checks survivors.
- pre-launch-audit — 3 agents: security, correctness, simplicity. For: reviewing experiment scripts / new code before GPU time.
- post-run-analysis — 3 agents: positive, adversarial, contextualizer. For: interpreting a completed run from multiple angles.
- blog-coherence — 2 agents: style, factual-alignment. For: verifying a new finding doesn't contradict existing site content.
Custom templates live in .claude/skills/deploy-team/templates/*.json.
Run artifacts
Each run creates .team-runs/<run-id>/:
config.json— resolved config (template + topic + context)thread.md— shared append-only thread (agents post findings for peers)context/— symlinked context files agents can readagents/<name>/prompt.txt— the exact prompt sentoutput.md— agent's final written findingsevents.jsonl— raw stream-json events (debug)
REPORT.md— combined report generated by the orchestrator
What ships with it
4 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.
- 10d ago First seen · 90 lines · 116 tokens per session scan A 232e6ed456c9
deploy-team is a skill published in the GitHub repository Rockielab/rockie-claude (21 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 116 tokens to every session and 964 once invoked, about $0.0006 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
auto-run
Autonomous personalized research loop. Use when the user wants to research a topic autonomously, run a research loop, start adaptive research, or use presets like technique-scout or cross-domain. Triggers on: 'auto run', 'research loop', 'autonomous research', 'run research', 'start research', 'adaptive research'.
autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports…
git-commit
A guided Git commit workflow that examines changes and creates a commit message using the Conventional Commits format, a shared style for labeling changes such as features, fixes, tests, or documentation.
pi-sync
Daily upstream-sync job for the pi Go port — fetch upstream pi, triage every change since the recorded pin, port what's in scope, verify idiomatic + parity via independent reviews, update the ledger, and push. Use for "sync with upstream", "porting job", or as the scheduled daily run.
pi-triage
Decide whether an upstream pi change needs porting to the Go port. Use when assessing upstream commits/PRs ("should we port X?"), or as the triage stage of /pi-sync. Outputs a WHY/WHAT/SCOPE verdict per change.
pi-parity-review
Adversarially verify that a ported change is faithful to the original pi implementation (TS source + published npm build). Use after porting upstream pi changes, or standalone on any area of this repo ("is X faithful to pi?").