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 agentscope-ai/QwenPaw-Data --skill query-odpsgit clone --depth 1 https://github.com/agentscope-ai/QwenPaw-DataWrote 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/agentscope-ai/qwenpaw-data/query-odps)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/query-odps"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/query-odps/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/agentscope-ai/qwenpaw-data/query-odps"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/query-odps.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 | $0.00081 | $0.00745 |
| Opus 5 | $0.00041 | $0.00373 |
| Sonnet 5 | $0.00016 | $0.00149 |
| Haiku 4.5 | $0.00008 | $0.00075 |
Grade A, and why
query-odps 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.
What it actually says
query-odps
MaxCompute 只读 SELECT 的域规则：怎么写对 SQL、怎么优化、怎么自检、怎么执行、失败如何回退。
禁止：直连 PyODPS；绕过标准执行通道自写 CSV。
规则索引
| 场景 | 读 |
|---|---|
| 写 SQL（方言） | references/dialect-rules.md |
| 分区 / MAX_PT | references/partition-semantics.md |
| SELECT 结构 | references/sql-correctness.md |
| 性能 / 扫描 | references/performance.md |
| 业务字面量 → 库内值 | references/value-discovery.md |
| 失败回退 | references/error-recovery.md |
核心原则
- 先取证 — 无元数据不臆测表名、列名、口径。
- 过滤值与 JOIN 键先定再写 SQL — 时间范围、分区边界、维度筛选、业务字面量对应的库内取值等 全部确认 后再生成 SQL；不得在 WHERE 里留臆测的枚举或占位过滤值。未映射的字面量确认参考
value-discovery.md。涉及 JOIN 时同理：写 ON 之前先对齐左右两边的连接键——列名是否同一实体、类型是否一致、格式是否同口径（如user_id是否都带前缀、ds是yyyyMMdd还是yyyy-MM-dd、STRING 是否需TRIM/CAST）；禁止在未核实键格式时直接拼 JOIN。 - 分步查询，勿一次开大 SQL — 执行结果会落入 ODPS 临时表（如
cm_tmp_*），应拆成可快速校验的小步：探针 / 单表过滤 / 中间结果落表 → 再基于临时表做下一步。禁止 一次性提交超大、多表深 JOIN、长窗口的「一步到位」SQL；一旦口径或 JOIN 键有误，会等非常久才失败，用户无法及时发现问题。 - 性能纪律 + 执行前自检 — 写 SQL 前 必须读
references/performance.md（分区必过滤、列裁剪、先聚合再 JOIN 等）；定稿后再对照performance.md、partition-semantics.md等 references 自检，明显违规 不得执行。ODPS 按扫描量计费，忽视此项会导致超时或严重资源浪费。 - 只走标准执行通道 — SELECT 仅经平台提供的执行通道提交；禁止 PyODPS 直连或自写 CSV。结果落盘路径由运行环境决定；
truncated须在回复中说明；禁止把超大结果全量贴进对话。
What ships with it
6 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 · 30 lines · 81 tokens per session scan A 0f9c85fef78e
query-odps is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (71 stars, last pushed yesterday), licensed Apache-2.0. It adds 81 tokens to every session and 745 once invoked, about $0.0004 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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