runtime-guide

runtime-guide is a skill for Claude Code, Codex from agentscope-ai/QwenPaw-Data. It costs 55 tokens per session (2,204 once invoked), scanned A, original, Apache-2.0.

A required set of rules for carrying out data-analysis tasks, including where to save plans, source data, processed results, and final outputs.

In plain words
What is it for?
Use it when planning or running data analysis that creates files, reuses earlier results, handles exceptions, changes its plan, or checks its own output.
Why use it?
It keeps analysis work organised and traceable, while defining how to handle reused data, errors, plan changes, and quality checks.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/agentscope-ai/qwenpaw-data/runtime-guide
Any agent
npx skills add agentscope-ai/QwenPaw-Data --skill runtime-guide
Clone the repo
git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for runtime-guide

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/runtime-guide.svg)](https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/runtime-guide)
Your own site
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/runtime-guide"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/runtime-guide.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,204 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.02204
Opus 5 $0.00028 $0.01102
Sonnet 5 $0.00011 $0.00441
Haiku 4.5 $0.00006 $0.00220

Measured 5d ago against content hash 1b9ab48f7bee, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

runtime-guide 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 5d 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.

packages/qwenpaw-data-skills/skills/runtime/runtime-guide/SKILL.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

runtime-guide


1. 产物落盘规范

1.1 工作目录

每次执行都使用 QwenPaw Data runtime 指定的当前产物目录。本文用 <current_artifacts> 表示:

普通对话:       <workspace>/artifacts/<session_id>
TaskGraph 节点: <workspace>/artifacts/<session_id>/<graph_id>/<node_id>

当前执行目录内结构如下:

<current_artifacts>/
├── plan.yaml                 # 原始 plan(来自 planner,不修改)
├── plan_v1.yaml              # 第一次修改后的 plan(如有)
├── plan_v2.yaml              # 第二次修改后的 plan(如有)
├── steps/                    # 步骤结果(每步完成后写入)
├── data/                     # 数据产物
│   ├── raw/                  # 原始获取的数据(从数据源拉取的原始结果)
│   └── processed/            # 计算处理后的数据(衍生指标、数据清洗结果等)
└── result.yaml               # 最终结果(执行完成后写入)
  • plan.yaml 是原始计划,始终保留不修改
  • 执行过程中如需调整计划,生成 plan_v1.yamlplan_v2.yaml... 按修改顺序递增
  • 执行时始终以最新版本的 plan 为准
  • session_id 必须使用 runtime 提供的当前值,不自行生成
  • 仅当 runtime 明确提供当前 graph_idnode_id 时才使用节点子目录;普通 对话直接使用 session 目录
  • 不在 workspace 根或未隔离的 artifacts/ 下创建任务目录

1.2 数据文件

类型 存放位置 命名
原始数据 <current_artifacts>/data/raw/ <数据描述>.<ext>
计算结果 <current_artifacts>/data/processed/ <分析内容>_<结果描述>.<ext>
  • 文件命名应自描述,能看出内容是什么
  • 列名/字段名须有业务含义(如 date, dau, dau_wow, is_anomaly),不使用 col1, col2
  • 每个关键的计算结果都要落盘到 data/processed/,包括清洗后的数据文件、衍生指标、归因结果、异常检测结果、维度交叉表等,确保结论可溯源、可复现

示例:

<current_artifacts>/data/raw/dau_daily_202603.csv
<current_artifacts>/data/processed/channel_attribution_result.csv

1.3 步骤结果

每个分析步骤完成后,落盘一份步骤结果,服务于过程审查和结果溯源。存放在当前执行 目录的 steps/ 子目录下:

<current_artifacts>/steps/
├── step_01_<步骤描述>.yaml
├── step_02_<步骤描述>.yaml
└── ...

每份步骤结果包含:

  • 做了什么:本步骤执行的操作描述
  • 产出文件:涉及的数据文件、中间结果的路径索引
  • 代码:本步骤执行的关键代码或脚本(如有)
  • 结论:本步骤的分析结论或发现

1.4 最终结果

执行完成后,在 <current_artifacts>/result.yaml 产出最终结果,包含:

  • 完成状态:全部完成 / 部分完成 / 失败
  • 核心结论:对分析目标的直接回答
  • 支撑数据:结论依赖的数据文件
  • 未解决问题:数据缺失、结果矛盾、未追踪的线索
  • 后续建议:建议深入分析的方向

Read the full file on GitHub · 188 lines

Changes

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.

  1. 5d ago First seen · 188 lines · 55 tokens per session scan A 1b9ab48f7bee

Subscribe to this mod's changes

runtime-guide is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (61 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 2,204 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

matryca-python-standards

Apply Matryca Python 3.12+, uv, typing, validation, concurrency, filesystem-safety, logging, and verification standards. Use whenever creating or modifying Python source or tests.

MarcoPorcellato/matryca-plumber · 46 tokens

python-mcp-server-expert

Expert assistant for developing Model Context Protocol (MCP) servers in Python using FastMCP, mcp package, Pydantic, and async patterns. Use when building MCP tools, resources, prompts, or debugging Python MCP server issues.

timothywarner-org/context-engineering · 55 tokens

python-testing-patterns

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

CarlosVallejoRuiz/slurp · 38 tokens

financial-memo-writer

Write investment-research memos and articles in a sincere, contrarian, framework-first zh-TW (Taiwan-market) voice. Use when asked to draft, ghost-write, or continue an in-depth analysis (深度分析), a multi-topic weekly market note (週度市場筆記), or a short column/op-ed (短評/專欄), or to rewrite content into a…

HaoweiChan/tinboker · 0 tokens

pipeline-debug

Debug and A/B-test the podcast content pipeline (pipelines/services/podcast) offline — run one stored episode through the real extractor→writer→markdown pipeline with any OpenRouter model, score JSON reliability / Traditional-Chinese fidelity / ticker+tag linking / chapter consolidation, find genuinely-failed…

HaoweiChan/tinboker · 94 tokens

threads-writer

Draft or rewrite Threads (脆) posts in TinBoker's voice — an openly automated account with one judgment worth stating and no first person (never "我"), not a social-media copywriter and not a news summary. Use when asked to turn a podcast episode into a Threads post, write/rewrite 脆文, fix a draft that reads like AI or…

HaoweiChan/tinboker · 115 tokens