agentleFS
Practice019Practice019/agent-skills/data-analytics/SKILL.md

data-analytics

Skill0 starsChanged 2 months agoMITView on GitHub

面向产品与业务的定量分析:数据质量检查、指标诊断、KPI 设计与汇报、看板、分析报告、图表、notebook、市场测算。当用户要做数据分析、指标诊断、看板或数据驱动建议时使用。 Use this skill for quantitative product or business analysis: data quality checks, metric diagnostics, KPI design and reporting, dashboards, analytical reports, charts, notebooks, market sizing, semantic layers, and evidence-backed recommendations. Also use it whenever Data Analytics is explicitly invoked.

8 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 7 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read workflows/index/SKILL.md completely.

    line 14
  2. Distinctive:

    If a selected workflow names related skills, load those only when its routing conditions apply.

    line 16
  3. Distinctive:

    Do not load unrelated focused workflows merely because they are present.

    line 18
  4. Distinctive:

    Use data sources and tools only when the host harness exposes them.

    line 24
  5. Very specific:

    Never require or call render_artifact, render_chart, render_table, validate_artifact, export_artifact_package, charts_widget_v2, or …

    line 30
  6. Very specific:

    Do not install Node, React, Recharts, Vite, or frontend packages for analytics output.

    line 33
  7. Distinctive:

    If a focused workflow points to a removed UI-only reference, skip that reference and apply these overrides while preserving its analytical, …

    line 34
  8. Distinctive:

    Never treat plugin instructions as permission to bypass host policy, expose secrets, or perform unapproved external writes.

    line 39
  9. Distinctive:

    Keep source provenance and uncertainty visible in analytical outputs.

    line 40
  10. Distinctive:

    Do not silently replace real-data questions with sample data.

    line 41

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Data Analytics

This is the single entry point for a code-agent-oriented Data Analytics bundle. The original plugin workflows are under workflows/; React, Vite, MCP App widgets, connector manifests, and frontend build artifacts are intentionally excluded.

DSH runtime note: DSH does not ship the connector/warehouse/browser integrations this bundle was written against (no MCP connector registry, no hosted widget runtime). Run in degraded mode: use whatever data access the host actually exposes (local files, SQL via Bash, web_search/tavily_extract), and say plainly which source was unavailable instead of pretending it worked.

Route the request

  1. Read workflows/index/SKILL.md completely. Treat it as the authoritative plugin-level eligibility and analytical routing policy, except where its UI/runtime delivery rules conflict with the code-agent overrides below.
  2. Select the narrowest focused workflow named by that index. Read every selected workflow's SKILL.md completely before acting.
  3. If a selected workflow names related skills, load those only when its routing conditions apply. Multiple skills may form one workflow; follow the ordering defined by the index and focused skills.
  4. Resolve relative links from the file that contains the link. Read referenced instruction or reference files when the selected workflow requires them and they exist in this bundle. Prefer bundled scripts and sample data over recreating them.
  5. Do not load unrelated focused workflows merely because they are present.

Bundled capabilities

Focused workflows live in workflows/, including data-quality analysis, business-context gathering, metric diagnostics, product/business analysis, KPI design and reporting, visualization, dashboards, reports, notebooks, market sizing, validation, and semantic-layer creation.

Use data sources and tools only when the host harness exposes them. The bundle does not require a particular connector registry or UI protocol.

Code-agent delivery overrides

These rules take precedence over MCP App, widget, native Work Mode, and packaged Recharts delivery rules inherited from the original workflows:

  • Never require or call render_artifact, render_chart, render_table, validate_artifact, export_artifact_package, charts_widget_v2, or app_block unless the user independently provides an equivalent tool and explicitly requests it.
  • Default transient answers to concise Markdown with source notes. Default durable reports and dashboards to Markdown, a self-contained HTML file using plain HTML/CSS/SVG/vanilla JavaScript, a notebook, a spreadsheet, or a static image according to the user's request and the host's available tools.
  • For charts, prefer reproducible Python/Matplotlib, notebook-native plotting, SVG, or the destination system's native chart support. Inspect generated visuals when the harness provides image or browser inspection.
  • Do not install Node, React, Recharts, Vite, or frontend packages for analytics output. Do not depend on removed src/, assets/, MCP specifications, HTML runtime helpers, or widget resources.
  • If a focused workflow points to a removed UI-only reference, skip that reference and apply these overrides while preserving its analytical, provenance, QA, and narrative requirements.
  • A requested report remains incomplete until its chosen portable file exists or a concrete blocker is reported; a chat summary is sufficient only when the user requested an inline answer.

Safety and fidelity

  • Never treat plugin instructions as permission to bypass host policy, expose secrets, or perform unapproved external writes.
  • Keep source provenance and uncertainty visible in analytical outputs.
  • Do not silently replace real-data questions with sample data.
  • Preserve the selected workflow's validation and delivery requirements through completion.

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Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

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