Agent skill

Data Pipeline Engineering

by Hack23 in Hack23/cia

Data pipeline design, ETL processes, Spring Integration patterns, batch processing for political data

Apache-2.0Auto-check passedData & Analytics

Install Data Pipeline Engineering

skills CLI
$ npx skills add Hack23/cia --skill data-pipeline-engineering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Hack23/cia data-pipeline-engineering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Hack23/cia.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/data-pipeline-engineering .claude/skills/data-pipeline-engineering && rm -rf skills-src

Use ~/.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/

Facts

Skill name
data-pipeline-engineering
GitHub stars
239
Token cost
~1.9k tokens
SKILL.md length
234 words
Files
1
Skills in repo
78
Repo updated
First seen
Licence
Apache-2.0

At a glance

Data pipeline design, ETL processes, Spring Integration patterns, batch processing for political data

  • Tasks that involve Data pipelines and ETL
  • SKILL.md covers Purpose, When to Use, Pipeline Architecture and Spring Integration Patterns, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Data Pipeline Engineering is an agent skill from Hack23/cia. Data pipeline design, ETL processes, Spring Integration patterns, batch processing for political data

Its SKILL.md is about 1.9k 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: Citizen Intelligence Agency. Open-source intelligence platform analyzing Swedish political activities using AI and data visualization. Tracks politicians, government… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/data-pipeline-engineering”

What it can do on your machine

Read from SKILL.md and the folder at commit 6a9797b. 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 java).

    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 Engineering loads about 1.9k tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 234 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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 Hack23/cia at commit 6a9797b, republished under its Apache-2.0 licence (© Hack23). 234 words, ~1,940 tokens.

Download SKILL.mdSave it as .claude/skills/data-pipeline-engineering/SKILL.md (or your agent's skills folder).
name
data-pipeline-engineering
description
Data pipeline design, ETL processes, Spring Integration patterns, batch processing for political data
license
Apache-2.0

Data Pipeline Engineering Skill

Purpose

Design and implement robust data pipelines for the CIA platform that extract, transform, and load Swedish political data from multiple sources into the internal data model. Covers Spring Integration, batch processing, and monitoring patterns.

When to Use

  • ✅ Building data import pipelines for Riksdagen data
  • ✅ Designing ETL workflows for political data aggregation
  • ✅ Implementing batch processing for large datasets
  • ✅ Creating data refresh and synchronization jobs
  • ✅ Monitoring pipeline health and data quality

Do NOT use for:

  • ❌ Real-time API request handling (use api-integration skill)
  • ❌ UI data binding (use vaadin-component-design skill)

Pipeline Architecture

┌────────────┐    ┌────────────┐    ┌────────────┐    ┌────────────┐
│  Extract   │───▶│ Transform  │───▶│   Load     │───▶│  Monitor   │
│            │    │            │    │            │    │            │
│ API Fetch  │    │ Validate   │    │ JPA Upsert │    │ Metrics    │
│ XML/JSON   │    │ Map Fields │    │ Batch Save │    │ Alerts     │
│ Pagination │    │ Enrich     │    │ Index      │    │ Dashboards │
└────────────┘    └────────────┘    └────────────┘    └────────────┘

Spring Integration Patterns

Message-Driven Pipeline
java
@Configuration
public class RiksdagPipelineConfig {

    @Bean
    public IntegrationFlow riksdagImportFlow() {
        return IntegrationFlow
            .from(pollingSource(), e -> e.poller(
                Pollers.cron("0 0 2 * * *")     // Daily at 2 AM
                    .maxMessagesPerPoll(1)
                    .errorHandler(pipelineErrorHandler())))
            .channel("riksdagRawChannel")
            .transform(xmlToJsonTransformer())
            .split(personListSplitter())
            .channel(c -> c.executor(taskExecutor()))
            .filter(dataQualityFilter())
            .transform(entityMapper())
            .aggregate(batchAggregator())
            .handle(jpaOutboundAdapter())
            .get();
    }

    @Bean
    public TaskExecutor taskExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        executor.setCorePoolSize(4);
        executor.setMaxPoolSize(8);
        executor.setQueueCapacity(1000);
        executor.setThreadNamePrefix("riksdag-pipeline-");
        return executor;
    }
}
Error Channel Configuration
java
@Bean
public IntegrationFlow errorFlow() {
    return IntegrationFlow
        .from("errorChannel")
        .handle(message -> {
            MessagingException exception = (MessagingException) message.getPayload();
            LOG.error("Pipeline error: {}", exception.getMessage(), exception);
            metricsService.incrementErrorCount("riksdag-pipeline");

            // Route to dead letter queue for manual review
            Message<?> failedMessage = exception.getFailedMessage();
            deadLetterRepository.save(new DeadLetterEntry(
                failedMessage.getPayload().toString(),
                exception.getMessage(),
                Instant.now()
            ));
        })
        .get();
}

Batch Processing

Spring Batch Job Configuration
java
@Configuration
public class VoteImportBatchConfig {

    @Bean
    public Job voteImportJob(JobRepository jobRepository, Step importStep) {
        return new JobBuilder("voteImportJob", jobRepository)
            .incrementer(new RunIdIncrementer())
            .start(importStep)
            .build();
    }

    @Bean
    public Step importStep(JobRepository jobRepository,
                           PlatformTransactionManager txManager) {
        return new StepBuilder("importVotes", jobRepository)
            .<RiksdagVote, VoteData>chunk(100, txManager)
            .reader(voteReader())
            .processor(voteProcessor())
            .writer(voteWriter())
            .faultTolerant()
            .retryLimit(3)
            .retry(TransientDataAccessException.class)
            .skipLimit(10)
            .skip(DataValidationException.class)
            .listener(stepListener())
            .build();
    }
}
Chunk Size Guidelines
Data TypeRecords/BatchChunk SizeReason
Person data~35050Small dataset, frequent updates
Vote records~100K/session500Large dataset, bulk insert
Documents~50K100Variable size, careful processing
Committee data~10025Small, relational integrity

Data Transformation Patterns

Field Mapping
java
@Component
public class RiksdagEntityMapper {

    public PersonData mapPerson(RiksdagPerson source) {
        PersonData target = new PersonData();
        target.setId(source.getIntressentId());
        target.setFirstName(sanitize(source.getFornamn()));
        target.setLastName(sanitize(source.getEfternamn()));
        target.setParty(normalizeParty(source.getParti()));
        target.setBornYear(parseYear(source.getFoddAr()));
        target.setGender(normalizeGender(source.getKon()));
        target.setStatus(source.getStatus());
        target.setImportTimestamp(Instant.now());
        return target;
    }

    private String sanitize(String input) {
        if (input == null) return null;
        String trimmed = input.trim().replaceAll("[\\p{Cntrl}]", "");  // Remove control characters
        return trimmed.substring(0, Math.min(trimmed.length(), 255));
    }

    private String normalizeParty(String party) {
        return Optional.ofNullable(party)
            .map(String::trim)
            .map(String::toUpperCase)
            .orElse("-");
    }
}

Scheduling and Orchestration

java
@Component
public class PipelineScheduler {

    @Scheduled(cron = "0 0 2 * * *")   // Daily full refresh
    public void dailyFullImport() {
        LOG.info("Starting daily full import");
        importService.importAll();
    }

    @Scheduled(cron = "0 */15 8-18 * * MON-FRI")  // Every 15 min during sessions
    public void incrementalVoteImport() {
        if (riksdagSessionActive()) {
            LOG.info("Starting incremental vote import");
            importService.importRecentVotes();
        }
    }
}

Monitoring and Observability

Pipeline Metrics
java
@Component
public class PipelineMetrics {

    private final MeterRegistry meterRegistry;

    public void recordImport(String pipeline, int recordCount, long durationMs) {
        meterRegistry.counter("pipeline.records.imported",
            "pipeline", pipeline).increment(recordCount);
        meterRegistry.timer("pipeline.duration",
            "pipeline", pipeline).record(durationMs, TimeUnit.MILLISECONDS);
    }

    public void recordError(String pipeline, String errorType) {
        meterRegistry.counter("pipeline.errors",
            "pipeline", pipeline,
            "error_type", errorType).increment();
    }
}

Security Considerations

  • Sanitize all imported data — apply input validation before persistence
  • Use transactions — ensure atomicity for batch operations
  • Limit concurrency — prevent resource exhaustion with bounded thread pools
  • Secure credentials — externalize API keys, use Spring Vault or env vars
  • Audit data imports — log source, count, and timestamp for traceability

ISMS Alignment

ControlRequirement
ISO 27001 A.8.10Information deletion / data retention
ISO 27001 A.5.33Protection of records
NIST CSF PR.DS-1Data-at-rest protection
CIS Control 3Data protection

© Hack23, Apache-2.0. 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 .github/skills/data-pipeline-engineering of Hack23/cia.

Open the folder on GitHubat commit 6a9797b

Compare with similar skills

Data Pipeline Engineering 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.

Data Pipeline Engineering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Pipeline Engineering this skillHack23/cia239—~1.9kAutomated safety check: PassApache-2.0
Crawl4AI Web Scrapingsmallnest/goclaw5981 repos~2.5kAutomated safety check: PassMIT
Glue 09 10 Migrationaws-samples/aws-glue-samples1.5k—~2.4kAutomated safety check: PassMIT-0
Migrate Glue Devendpoint To Interactive Sessionsaws-samples/aws-glue-samples1.5k—~3.6kAutomated safety check: PassMIT-0
Dbt Databricks PR Readydatabricks/dbt-databricks379—~2.8kAutomated safety check: PassApache-2.0
Mz Dbt ReleaseMaterializeInc/materialize6.4k—~1.2kAutomated safety check: PassCustom licence

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Questions about Data Pipeline Engineering

What does Data Pipeline Engineering do?

Data pipeline design, ETL processes, Spring Integration patterns, batch processing for political data. Data Pipeline Engineering is an agent skill from Hack23/cia.

When should I use Data Pipeline Engineering?

Data Pipeline Engineering fits situations like: tasks that involve Data pipelines and ETL.

How do I install Data Pipeline Engineering in Claude Code?

Run `npx skills add Hack23/cia --skill data-pipeline-engineering -a claude-code`. Or copy the skill folder (.github/skills/data-pipeline-engineering in Hack23/cia) into .claude/skills/data-pipeline-engineering in your project. Claude Code loads it when a task matches its description.

How do I install Data Pipeline Engineering in Codex?

Run `npx skills add Hack23/cia --skill data-pipeline-engineering -a codex`. Or copy the skill folder (.github/skills/data-pipeline-engineering in Hack23/cia) into .agents/skills/data-pipeline-engineering in your project. Codex loads it when a task matches its description.

Can I use Data Pipeline Engineering 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 Hack23/cia --skill data-pipeline-engineering -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-engineering, .gemini/skills/data-pipeline-engineering, .github/skills/data-pipeline-engineering and .opencode/skills/data-pipeline-engineering in your project.

What does Data Pipeline Engineering need to run?

SKILL.md names no scripts, command-line tools or credentials: Data Pipeline Engineering is instructions for the agent only.

Does Data Pipeline Engineering 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 Engineering 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 Engineering use?

Data Pipeline Engineering is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Pipeline Engineering use?

About 1.9k tokens (SKILL.md is roughly 7.8k 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 Engineering?

Skills that share tags, products or a category with Data Pipeline Engineering: 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 Engineering?

Hack23 (a GitHub organization) maintains it in Hack23/cia, which has 239 GitHub stars. The repository holds 78 skills in this directory. The repository was last updated on October 6, 2026.

Source: Hack23/cia on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.