Crawl4AI Web Scraping
smallnest/goclaw
Scrapes sites, handles JavaScript-heavy pages and extracts structured data with Crawl4AI, through its crwl CLI or Python SDK, including schema-based extraction without an LLM.
ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
Install Data Pipeline Patterns
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install vibeeval/vibecosystem data-pipeline-patterns --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-pipeline-patterns .claude/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .claude/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patternsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install vibeeval/vibecosystem data-pipeline-patterns --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-pipeline-patterns .agents/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .agents/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install vibeeval/vibecosystem data-pipeline-patterns --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-pipeline-patterns .cursor/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .cursor/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/vibeeval/vibecosystem.git --path skills/data-pipeline-patterns--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install vibeeval/vibecosystem data-pipeline-patterns --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-pipeline-patterns .gemini/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .gemini/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install vibeeval/vibecosystem data-pipeline-patternsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-pipeline-patterns .github/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .github/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install vibeeval/vibecosystem data-pipeline-patterns --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-pipeline-patterns .opencode/skills/data-pipeline-patterns && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "data-pipeline-patterns" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns into .opencode/skills/data-pipeline-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-pipeline-patterns", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
data-pipeline-patterns- GitHub stars
- 531
- Token cost
- ~803 tokens
- SKILL.md length
- 148 words
- Files
- 1
- Skills in repo
- 144
- Repo updated
- First seen
- Licence
- MIT
At a glance
ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
- Tasks that involve Data pipelines and ETL
- SKILL.md covers ETL vs ELT Decision, Batch vs Streaming, Idempotency Patterns and Data Quality Framework, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Pipeline Patterns is an agent skill from vibeeval/vibecosystem. ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
Its SKILL.md is about 800 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data pipelines and ETL. The repository describes itself as: AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. The licence is MIT.
When your agent uses it
- Tasks that involve Data pipelines and ETL
Example prompts
- “/data-pipeline-patterns”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Data Pipeline Patterns loads about 803 tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 148 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 148 words, ~803 tokens.
Download SKILL.mdSave it as
.claude/skills/data-pipeline-patterns/SKILL.md (or your agent's skills folder).- name
- data-pipeline-patterns
- description
- ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
Data Pipeline Patterns
ETL vs ELT Decision
| Kriter | ETL | ELT |
|---|---|---|
| Transform location | Pipeline'da | Data warehouse'da |
| Data volume | Küçük-orta | Büyük |
| Flexibility | Düşük | Yüksek |
| Cost | Compute-heavy | Storage-heavy |
| Use case | Legacy, compliance | Modern analytics |
Batch vs Streaming
| Kriter | Batch | Streaming |
|---|---|---|
| Latency | Dakika-saat | Saniye-milisaniye |
| Complexity | Düşük | Yüksek |
| Cost | Düşük | Yüksek |
| Use case | Reporting, ETL | Real-time alerts, dashboards |
| Tool | Airflow, dbt | Kafka Streams, Flink |
Idempotency Patterns
# Pattern 1: Upsert
INSERT INTO target (id, name, updated_at)
VALUES (%(id)s, %(name)s, %(ts)s)
ON CONFLICT (id) DO UPDATE SET
name = EXCLUDED.name,
updated_at = EXCLUDED.updated_at
# Pattern 2: Partition overwrite
DELETE FROM target WHERE partition_date = '2026-03-14';
INSERT INTO target SELECT * FROM staging WHERE partition_date = '2026-03-14';
# Pattern 3: Checkpoint
last_checkpoint = get_checkpoint('pipeline_x')
new_data = source.query(f"WHERE updated_at > '{last_checkpoint}'")
process(new_data)
save_checkpoint('pipeline_x', max(new_data.updated_at))Data Quality Framework
import pandera as pa
schema = pa.DataFrameSchema({
"user_id": pa.Column(int, pa.Check.gt(0), nullable=False),
"email": pa.Column(str, pa.Check.str_matches(r'^.+@.+\..+$')),
"age": pa.Column(int, pa.Check.in_range(0, 150), nullable=True),
"created_at": pa.Column(pa.DateTime, pa.Check.less_than_or_equal_to(pd.Timestamp.now()))
})
validated_df = schema.validate(df) # Fail on invalid dataQuality Dimensions
| Dimension | Kontrol | Tool |
|---|---|---|
| Completeness | NULL ratio < threshold | Great Expectations |
| Accuracy | Value range checks | pandera |
| Freshness | Last update < SLA | Airflow sensor |
| Uniqueness | Duplicate check | SQL DISTINCT |
| Consistency | Cross-table referential integrity | dbt test |
Pipeline Orchestration
# Airflow DAG
from airflow import DAG
from airflow.operators.python import PythonOperator
with DAG('daily_etl', schedule='0 6 * * *', catchup=False) as dag:
extract = PythonOperator(task_id='extract', python_callable=extract_fn)
transform = PythonOperator(task_id='transform', python_callable=transform_fn)
load = PythonOperator(task_id='load', python_callable=load_fn)
validate = PythonOperator(task_id='validate', python_callable=validate_fn)
extract >> transform >> load >> validateChecklist
- Pipeline idempotent (rerun safe)
- Data quality checks her adımda
- Dead letter queue (failed records)
- Monitoring + alerting aktif
- Schema evolution handled
- Backfill mekanizması var
- Retry logic (exponential backoff)
- Data lineage tracked
Anti-Patterns
- Pipeline'da hardcoded credentials
- Idempotent olmayan transform
- Data quality check'siz load
- Monolithic pipeline (parçala)
- Silent failure (error swallowing)
© vibeeval, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/data-pipeline-patterns of vibeeval/vibecosystem.
Open the folder on GitHubat commit 3b763b1
Compare with similar skills
Data Pipeline Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Data Pipeline Patterns this skillvibeeval/vibecosystem | 531 | — | ~803 | Automated safety check: Pass | MIT | |
| Crawl4AI Web Scrapingsmallnest/goclaw | 598 | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Glue 09 10 Migrationaws-samples/aws-glue-samples | 1.5k | — | ~2.4k | Automated safety check: Pass | MIT-0 | |
| Migrate Glue Devendpoint To Interactive Sessionsaws-samples/aws-glue-samples | 1.5k | — | ~3.6k | Automated safety check: Pass | MIT-0 | |
| Dbt Databricks PR Readydatabricks/dbt-databricks | 379 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Mz Dbt ReleaseMaterializeInc/materialize | 6.4k | — | ~1.2k | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Data Pipeline Patterns
What does Data Pipeline Patterns do?
ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration. Data Pipeline Patterns is an agent skill from vibeeval/vibecosystem.
When should I use Data Pipeline Patterns?
Data Pipeline Patterns fits situations like: tasks that involve Data pipelines and ETL.
How do I install Data Pipeline Patterns in Claude Code?
Run `npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a claude-code`. Or copy the skill folder (skills/data-pipeline-patterns in vibeeval/vibecosystem) into .claude/skills/data-pipeline-patterns in your project. Claude Code loads it when a task matches its description.
How do I install Data Pipeline Patterns in Codex?
Run `npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a codex`. Or copy the skill folder (skills/data-pipeline-patterns in vibeeval/vibecosystem) into .agents/skills/data-pipeline-patterns in your project. Codex loads it when a task matches its description.
Can I use Data Pipeline Patterns in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vibeeval/vibecosystem --skill data-pipeline-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-pipeline-patterns, .gemini/skills/data-pipeline-patterns, .github/skills/data-pipeline-patterns and .opencode/skills/data-pipeline-patterns in your project.
What does Data Pipeline Patterns need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Pipeline Patterns is instructions for the agent only. Our summary lists: Python 3.
Does Data Pipeline Patterns access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Data Pipeline Patterns safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Data Pipeline Patterns use?
Data Pipeline Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data Pipeline Patterns use?
About 803 tokens (SKILL.md is roughly 3.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Data Pipeline Patterns?
Skills that share tags, products or a category with Data Pipeline Patterns: Crawl4AI Web Scraping (smallnest/goclaw, 598 stars), Glue 09 10 Migration (aws-samples/aws-glue-samples, 1.5k stars), Migrate Glue Devendpoint To Interactive Sessions (aws-samples/aws-glue-samples, 1.5k stars) and Dbt Databricks PR Ready (databricks/dbt-databricks, 379 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Pipeline Patterns?
vibeeval (a GitHub user) maintains it in vibeeval/vibecosystem, which has 531 GitHub stars. The repository holds 144 skills in this directory. The repository was last updated on August 8, 2026.
Source: vibeeval/vibecosystem on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.