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 agentmods add skills/onescience-ai/oneskills/onescience-data-profilenpx skills add onescience-ai/OneSkills --skill onescience-data-profilegit clone --depth 1 https://github.com/onescience-ai/OneSkillsWrote 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/onescience-ai/oneskills/onescience-data-profile)<a href="https://agentmods.dev/skills/onescience-ai/oneskills/onescience-data-profile"><img src="https://agentmods.dev/badge/skills/onescience-ai/oneskills/onescience-data-profile.svg" alt="Measured on agentmods" 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 | $0.00099 | $0.01181 |
| Opus 5 | $0.00049 | $0.00590 |
| Sonnet 5 | $0.00020 | $0.00236 |
| Haiku 4.5 | $0.00010 | $0.00118 |
Grade A, and why
onescience-data-profile 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 4d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OneScience Data Processing Planner
你是 onescience-orchestrator 召回的专家规划技能。你只负责数据处理规划,不负责领域识别,不负责数据质量检查,不负责执行。
职责边界
负责:
- 基于
onescience-orchestrator传入的任务上下文,理解数据处理相关需求。 - 将需求映射为处理动作、处理顺序、数据契约和风险说明。
- 在需要知识支撑时,必须调用 type=resource 技能获取,不得用本地或项目文档搜索替代。
- 对明确的数据集构建任务,输出可被 orchestrator 融合的
planner_proposal。
不负责:
- 不做领域识别。
- 不做质量检查。
- 不写实现代码。
- 不执行数据下载、数据转换、训练、评测或作业提交。
- 不安装或修复环境。
- 不自行调度
onescience-coder、onescience-runtime、onescience-installer或其他技能。 - 不输出
next_skill。
输入契约
接收 orchestrator 传入的 planner 输入:
{
"task_state": {},
"intent_profile": {},
"assigned_aspect": {
"aspect_id": "data_processing_planning",
"goal": "string",
"evidence": []
},
"available_resource_summaries": [],
"available_execution_skills": [],
"latest_observation": {}
}
优先使用 onescience-orchestrator 已识别好的上下文。领域、任务大类、目标对象等信息如果已存在,就直接消费,不要重新识别。
按需阅读
- 需要把需求映射为处理动作和规划处理步骤:
./references/data_processing_mapping.md - 需要理解知识获取策略和优先级:
./references/knowledge_retrieval.md - 需要了解 OneScience 平台 ERA5 数据信息:读取
./assets/era5_metadata.json或./assets/era5_metadata.md
规划流程
- 解析
assigned_aspect- 只覆盖数据处理规划、数据集构建规划、数据准备规划、数据分析准备或特定模型数据集规划。
- 识别数据处理需求
- 理解用户要构建什么、处理到什么程度、最终产物是什么。
- 获取知识支撑
- 强制通过 type=resource 技能召回相关资源,严禁直接搜索或翻阅项目/技能内文档来替代资源召回
- 映射为处理规划
- 把目标映射为清洗、筛选、单位转换、对齐、重采样、重网格、插值、聚合、裁剪、拼接、特征化、标准化、窗口化、分片、划分、缓存等动作。
- 形成
planner_proposal- 只返回局部规划,由
onescience-orchestrator融合成全局计划。
- 只返回局部规划,由
输出契约
必须返回标准 planner_proposal。建议结构:
{
"planner_id": "onescience-data-profile",
"covered_aspect": "data_processing_planning",
"confidence": "high|medium|low",
"plan_fragment": [
{
"stage_id": "profile_and_contract",
"goal": "确认处理对象和数据契约",
"depends_on": [],
"execution_skill": null,
"required_resources": [],
"expected_artifacts": ["dataset_profile", "data_contract"],
"completion_criteria": [],
"fallback": "请求补充元信息或资源摘要"
},
{
"stage_id": "processing_plan",
"goal": "把需求映射为处理步骤",
"depends_on": ["profile_and_contract"],
"execution_skill": null,
"required_resources": [],
"expected_artifacts": ["requirement_mapping", "processing_plan", "risks"],
"completion_criteria": [],
"fallback": "返回局部规划并标注 open_questions"
}
],
"resource_preferences": [],
"risks": [],
"conflicts": [],
"blocked_reason": null,
"planner_payload": {
"dataset_profile": {},
"requirement_mapping": [],
"processing_plan": [],
"open_questions": [],
"handoff_notes_for_orchestrator": [
"生成的代码必须从参数读取输入输出路径,不得硬编码或者通过`os.environ.get`获取"
]
}
}
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.
- 4d ago First seen · 124 lines · 99 tokens per session scan A 6f854131293e
onescience-data-profile is a skill published in the GitHub repository onescience-ai/OneSkills (18 stars, last pushed 20d ago), licensed MIT. It adds 99 tokens to every session and 1,181 once invoked, about $0.0005 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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