Borrowing it
Nothing to install: this file belongs to Human-Agent-Society/CORAL. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Human-Agent-Society/CORAL/main/.claude/skills/coral-new-task/SKILL.mdgit 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/coral-new-task)<a href="https://agentmods.dev/skills/human-agent-society/coral/coral-new-task"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/coral-new-task/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-new-task"><img src="https://agentmods.dev/badge/skills/human-agent-society/coral/coral-new-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 117 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00138 | $0.03292 |
| Opus 5 | $0.00069 | $0.01646 |
| Sonnet 5 | $0.00028 | $0.00658 |
| Haiku 4.5 | $0.00014 | $0.00329 |
Grade A, and why
coral-new-task 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating a new CORAL task
A CORAL task is three things that must line up:
examples/<task>/
├── task.yaml # config: name, description, grader entrypoint, agent count
├── seed/ # starter code agents see when they begin (the repo_path)
│ └── solution.py
└── grader/ # standalone Python package
├── pyproject.toml
└── src/<task>_grader/
├── __init__.py
└── grader.py # class Grader(TaskGrader): ...
The packaged form is the only supported form. The package gives the grader its own venv and ships everything the eval needs — grader code, helper modules, and hidden data (see "Hidden data" below).
Reference implementations
Look at these before writing anything new — copy the closest one and edit:
| Reference | When to copy it |
|---|---|
| examples/erdos/ | Minimal packaged grader, single grader file, numpy-only deps |
| examples/dna_design/ | Packaged grader with bundled data files (importlib.resources) and [ml] optional-deps for heavy libs |
| examples/swebench-verified/ | Tiered eval (different instance counts per tier), private answer keys, harbor integration |
| examples/circle_packing/ | Smallest packaged task end-to-end — single solution file, single grader file |
| examples/mnist/ | Packaged grader with a hidden answer key (note: secret data belongs under grader.private in a taskdata/ sibling of grader/, never inside the grader package) |
1. The seed
Whatever lives in seed/ is what the agent sees on first checkout — it's the working directory the grader will later score. The contract between seed/ and the grader is the program file: a Python file with a function the grader imports and calls.
The convention across examples is:
solution.py(orinitial_program.py) defining a top-levelrun()function.- The grader passes
program_file: "solution.py"viagrader.args. run()'s signature is whatever the grader expects — usually() -> resultor(input_path) -> result.
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 · 234 lines · 138 tokens per session scan A 8f586badcd29
coral-new-task is a skill published in the GitHub repository Human-Agent-Society/CORAL (975 stars, last pushed 3d ago), licensed Apache-2.0. It adds 138 tokens to every session and 3,292 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.
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