vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill sql-queries-window-functions命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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基于 SOC 职业分类
| name | sql-queries-window-functions |
| description | Sub-skill of sql-queries: Window Functions (+4). |
| version | 1.0.0 |
| category | data-analytics |
| type | reference |
| scripts_exempt | true |
-- Ranking
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC)
RANK() OVER (PARTITION BY category ORDER BY revenue DESC)
() ( score )
(revenue) ( date_col UNBOUNDED PRECEDING ) running_total
(revenue) ( date_col PRECEDING ) moving_avg_7d
(, ) ( entity date_col) prev_value
(, ) ( entity date_col) next_value
(status) ( user_id created_at UNBOUNDED PRECEDING UNBOUNDED FOLLOWING)
(status) ( user_id created_at UNBOUNDED PRECEDING UNBOUNDED FOLLOWING)
revenue (revenue) () pct_of_total
revenue (revenue) ( category) pct_of_category
WITH
-- Step 1: Define the base population
base_users AS (
SELECT user_id, created_at, plan_type
FROM users
WHERE created_at >= DATE '2024-01-01'
AND status = 'active'
),
-- Step 2: Calculate user-level metrics
user_metrics AS (
SELECT
u.user_id,
u.plan_type,
COUNT(DISTINCT e.session_id) as session_count,
SUM(e.revenue) as total_revenue
FROM base_users u
LEFT JOIN events e ON u.user_id = e.user_id
GROUP BY u.user_id, u.plan_type
),
-- Step 3: Aggregate to summary level
summary AS (
SELECT
plan_type,
COUNT(*) as user_count,
AVG(session_count) as avg_sessions,
SUM(total_revenue) as total_revenue
FROM user_metrics
GROUP BY plan_type
)
SELECT * FROM summary ORDER BY total_revenue DESC;
WITH cohorts AS (
SELECT
user_id,
DATE_TRUNC('month', first_activity_date) as cohort_month
FROM users
),
activity AS (
SELECT
user_id,
DATE_TRUNC('month', activity_date) as activity_month
FROM user_activity
)
SELECT
c.cohort_month,
COUNT(DISTINCT c.user_id) as cohort_size,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month THEN a.user_id
END) as month_0,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month + INTERVAL '1 month' THEN a.user_id
END) as month_1,
COUNT(DISTINCT CASE
WHEN a.activity_month = c.cohort_month + INTERVAL '3 months' THEN a.user_id
END) as month_3
FROM cohorts c
LEFT JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort_month
ORDER BY c.cohort_month;
WITH funnel AS (
SELECT
user_id,
MAX(CASE WHEN event = 'page_view' THEN 1 ELSE 0 END) as step_1_view,
MAX(CASE WHEN event = 'signup_start' THEN 1 ELSE 0 END) as step_2_start,
MAX(CASE WHEN event = 'signup_complete' THEN 1 ELSE 0 END) as step_3_complete,
MAX(CASE WHEN event = 'first_purchase' THEN 1 ELSE 0 END) as step_4_purchase
FROM events
WHERE event_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY user_id
)
SELECT
COUNT(*) as total_users,
SUM(step_1_view) as viewed,
SUM(step_2_start) as started_signup,
SUM(step_3_complete) as completed_signup,
SUM(step_4_purchase) as purchased,
ROUND(100.0 * SUM(step_2_start) / NULLIF(SUM(step_1_view), 0), 1) as view_to_start_pct,
ROUND(100.0 * SUM(step_3_complete) / NULLIF(SUM(step_2_start), 0), 1) as start_to_complete_pct,
ROUND(100.0 * SUM(step_4_purchase) / NULLIF(SUM(step_3_complete), 0), 1) as complete_to_purchase_pct
FROM funnel;
-- Keep the most recent record per key
WITH ranked AS (
SELECT
*,
ROW_NUMBER() OVER (
PARTITION BY entity_id
ORDER BY updated_at DESC
) as rn
FROM source_table
)
SELECT * FROM ranked WHERE rn = 1;
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.