Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Human-Agent-Society/CORAL/plugin install 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/coral-quickstart)<a href="https://agentmods.dev/skills/human-agent-society/coral/coral-quickstart"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/coral-quickstart/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/coral-quickstart"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/coral-quickstart.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 | $0.00185 | $0.01814 |
| Opus 5 | $0.00093 | $0.00907 |
| Sonnet 5 | $0.00037 | $0.00363 |
| Haiku 4.5 | $0.00018 | $0.00181 |
Grade C, and why
coral-quickstart scanned grade C with 2 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 11d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/Human-Agent-Society/CORAL/main/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/Human-Agent-Society/CORAL/main/install.sh | sh How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CORAL quickstart
CORAL is infrastructure for autonomous coding agents: you give it a codebase (seed/) and a grader (turns a commit into a number), and it spawns agents in isolated git worktrees that edit code, submit commits, and get scored on a shared leaderboard — looping to push the score up. The agents are the optimizer; your grader defines "better".
When to reach for CORAL
Good fit:
- You can express success as a number — accuracy, runtime ratio, pass rate, or a rubric-judge score for open-ended work.
- The work is iterative search: many attempts at one well-scoped problem (kernel/algorithm optimization, benchmark solving, prompt/program tuning, "make this function faster").
- You want parallel agents exploring independently and sharing what works.
Not a fit:
- One-shot tasks with no measurable objective.
- Work that needs a human judging every attempt (use a rubric-judge grader if a model can score it).
How a run is shaped
you provide: seed/ (starter code) + a grader (commit → number)
coral spawns: N agents, each in its own git worktree
each agent: edit code → `coral eval` → grader scores it → read leaderboard → repeat
shared state: attempts, notes, and skills are visible across agents in real time
Two things you build (seed/ + grader) and one thing you tune (how many agents, which model). Worktrees, scoring daemon, shared state, and restarts are handled for you.
Get running — four steps
1. Install the CLI
curl -fsSL https://raw.githubusercontent.com/Human-Agent-Society/CORAL/main/install.sh | sh
# or, if you have uv:
uv tool install git+https://github.com/Human-Agent-Society/CORAL.git
coral --help # verify
The installer grabs the latest coral release by default — that's what you want. (Only pin a specific release with CORAL_VERSION=<tag> before the curl if you have a reason to.)
2. Register a runtime (coral setup)
coral shells out to a coding-agent CLI (Claude Code, Codex, Cursor, Kiro, OpenCode) — each installed and authenticated separately. Tell coral which to use:
What ships with it
1 file 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.
- 11d ago First seen · 107 lines · 185 tokens per session scan C a3687c600c77
coral-quickstart is a skill published in the GitHub repository Human-Agent-Society/CORAL (972 stars, last pushed 3d ago), licensed Apache-2.0. It adds 185 tokens to every session and 1,814 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
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.