Evan
05bdf02169
* feat(workflows): add 13 workflow templates across engineering, business, and productivity Engineering: - bug-triage: reproduce path → root cause → fix plan - api-design: resource model → endpoints → OpenAPI spec - incident-postmortem: timeline → RCA → full postmortem report - test-generation: code analysis → edge cases → full test suite - refactor-plan: smell analysis → prioritised opportunities → migration plan Business: - competitor-analysis: profiles → SWOT → strategy report - product-spec: problem definition → user stories → full PRD - market-research: landscape → segments → research report Productivity/Thinking: - meeting-summary: raw notes → structured summary → follow-up email - decision-matrix: criteria → weighted scoring → recommendation memo - learning-plan: gap analysis → roadmap → week-1 day-by-day plan - job-application: job analysis → tailored resume → cover letter → interview prep - blog-post: research → outline → draft → SEO optimisation Closes #1912 on librefang/librefang * fix(workflows): overhaul existing 9 templates - data-pipeline: redesigned — original 'extract from URL' step was broken (LLMs cannot fetch URLs); replaced with paste-data approach (profile → clean_transform → analyse) with analysis_goal parameter - translate-polish: added target_language and register parameters; added back-translation step for accuracy verification - weekly-report: added team/audience parameters, richer extraction step, added Metrics and Notes sections - content-pipeline: added audience/tone parameters, added outline step between research and writing - content-review: fix category 'content' → 'creation' - customer-support: fix category 'support' → 'business' * style(workflows): convert all prompt_template strings to TOML multiline syntax Replace \n escape sequences with real newlines using triple-quote multiline strings ("""...""") across all 22 workflow templates. No content changes — formatting only. * style(hands): replace \n escape in reddit writer format example with multiline code block
83 lines
2.3 KiB
TOML
83 lines
2.3 KiB
TOML
id = "data-pipeline"
|
|
name = "Data Analysis & Transform"
|
|
description = "Clean, transform, analyse, and summarise raw data. Handles messy inputs: CSV, JSON, tables, or free-form text."
|
|
category = "engineering"
|
|
tags = ["data", "analysis", "transform", "etl"]
|
|
|
|
[i18n.zh]
|
|
name = "数据分析与转换"
|
|
description = "清洗、转换、分析并摘要原始数据，支持 CSV、JSON、表格或自由文本等杂乱输入。"
|
|
|
|
[[parameters]]
|
|
name = "raw_data"
|
|
description = "The raw data to process (paste CSV, JSON, table, or any structured text)"
|
|
param_type = "string"
|
|
required = true
|
|
|
|
[[parameters]]
|
|
name = "output_format"
|
|
description = "Desired output format for the transformed data (json, csv, markdown-table)"
|
|
param_type = "string"
|
|
required = false
|
|
default = "json"
|
|
|
|
[[parameters]]
|
|
name = "analysis_goal"
|
|
description = "What you want to understand or extract from the data"
|
|
param_type = "string"
|
|
required = false
|
|
default = "general summary and key insights"
|
|
|
|
[[steps]]
|
|
name = "profile"
|
|
prompt_template = """
|
|
You are a data engineer. Profile the following raw data:
|
|
1. Detected format and structure
|
|
2. Number of rows and columns/fields
|
|
3. Data types per field
|
|
4. Missing value count per field
|
|
5. Obvious data quality issues (duplicates, inconsistent formatting, out-of-range values)
|
|
6. Sample of first 5 rows for reference
|
|
|
|
Raw data:
|
|
{{raw_data}}
|
|
"""
|
|
|
|
[[steps]]
|
|
name = "clean_transform"
|
|
prompt_template = """
|
|
Clean and transform the raw data based on the profile below. Apply:
|
|
- Trim whitespace from string fields
|
|
- Normalise dates to ISO-8601
|
|
- Remove exact duplicate rows
|
|
- Convert empty strings to null
|
|
- Standardise inconsistent categorical values (e.g. 'Y'/'Yes'/'yes' → true)
|
|
- Flag but preserve rows with suspicious values (do not silently drop them)
|
|
|
|
Output the cleaned data in {{output_format}} format, followed by a change log listing every transformation applied.
|
|
|
|
Data profile:
|
|
{{profile}}
|
|
|
|
Raw data:
|
|
{{raw_data}}
|
|
"""
|
|
depends_on = ["profile"]
|
|
|
|
[[steps]]
|
|
name = "analyse"
|
|
prompt_template = """
|
|
Analyse the cleaned data to address the following goal: {{analysis_goal}}
|
|
|
|
Provide:
|
|
1. Key statistics (counts, totals, averages, distributions as appropriate)
|
|
2. Notable patterns or trends
|
|
3. Outliers or anomalies worth investigating
|
|
4. Correlations between fields (if applicable)
|
|
5. Top 3 actionable insights from the data
|
|
|
|
Cleaned data:
|
|
{{clean_transform}}
|
|
"""
|
|
depends_on = ["clean_transform"]
|