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 Human-Agent-Society/CORAL --skill running-coral-experimentsgit clone --depth 1 https://github.com/Human-Agent-Society/CORALWrote 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/human-agent-society/coral/running-coral-experiments)<a href="https://agentmods.dev/skills/human-agent-society/coral/running-coral-experiments"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/running-coral-experiments/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/human-agent-society/coral/running-coral-experiments"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/running-coral-experiments.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 | $0.00166 | $0.01266 |
| Opus 5 | $0.00083 | $0.00633 |
| Sonnet 5 | $0.00033 | $0.00253 |
| Haiku 4.5 | $0.00017 | $0.00127 |
Grade A, and why
running-coral-experiments 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 12d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Running CORAL experiments
You drive a run with five verbs: start → status → log/show → resume → stop. Everything else is a flag on those or a deeper topic in the references. Prefer coral <cmd> --help over guessing flags.
Prereq: a task (task.yaml + seed/ + grader package) that passes coral validate .. No task yet → that's the creating-a-coral-task skill. Each runtime CLI must be installed and authenticated → the setting-up-coral skill.
1. Launch
coral start -c task.yaml # auto-tmux session
coral start -c task.yaml agents.count=4 agents.model=opus # dotlist overrides (no quotes needed)
coral start -c task.yaml run.verbose=true run.ui=true # verbose logs + web dashboard
coral start -c task.yaml run.session=local # foreground, no tmux
- Dotlist overrides (
key.subkey=value) beattask.yamlfor this run only — the clean way to sweep count/model without editing the file. run.session:tmux(default, detachable) ·local(foreground) ·docker.- Each run lands in
results/<task-slug>/<timestamp>/; agents work in isolated git worktrees and the grader daemon scores their commits.
2. Monitor
coral status # agent health + leaderboard snapshot (the quick pulse)
coral runs # active runs across tasks; --all includes finished
coral ui --port 8420 # web dashboard: live leaderboard, logs, DAG
coral status answers "who's alive, how many evals, current best". If it looks healthy but scores never move, jump to budget classes + troubleshooting in references/scaling-and-ops.md.
3. Read results
coral log # top 20 real attempts by score
coral log -n 5 --recent # most recent instead of best
coral log --search "kernel" --agent agent-1
coral log --class grader_error # surface crashing graders (first stop when unhealthy)
coral show <hash> # one attempt: score, explanation, files changed
coral show <hash> --diff # full diff — see exactly what the leader did
What ships with it
2 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.
- 12d ago First seen · 86 lines · 166 tokens per session scan A e8cf7e678e01
running-coral-experiments is a skill published in the GitHub repository Human-Agent-Society/CORAL (972 stars, last pushed 3d ago), licensed Apache-2.0. It adds 166 tokens to every session and 1,266 once invoked, about $0.0008 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.
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cw-gates
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writing
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feishu
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verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
new-app
Scaffold a new Atomic Agents project from scratch — create the directory, pyproject.toml, env file, first agent, and a runnable entry point. Use when the user asks to start a new atomic-agents project from scratch, says "scaffold" / "new project" / "start from zero", or runs /atomic-agents:new-app.