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CI/CD Pipeline Generator Skill · a5c-ai | DecimalAI

a5c-ai/cicd-pipeline-generator

SkillScore92Maintained

Generate CI/CD pipelines for SDK build and release

DevOpsby a5c-ai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

DevOps & SRE pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/a5c-ai-cicd-pipeline-generator/SKILL.md

decimalai skills pull a5c-ai-cicd-pipeline-generator --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/a5c-ai-cicd-pipeline-generator/SKILL.md https://app.decimal.ai/s/a5c-ai-cicd-pipeline-generator/SKILL.md

Raw file: /s/a5c-ai-cicd-pipeline-generator/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-cicd-pipeline-generator.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

25 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 30d ago

Yes — +16% lift+17% tokens0% turns92% of whole cases pass with skill (23/25)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	29%	0%
case-10	✗→✓	▲ Improved	-3%	0%
case-14	✗→✓	▲ Improved	-18%	0%
case-15	✗→✓	▲ Improved	-6%	0%
case-03	✓→✓	＝ Same ✓	11%	0%

Showing 5 of 25 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

a5c-ai / babysitter

library/specializations/sdk-platform-development/skills/cicd-pipeline-generator/SKILL.md

View on GitHub ↗

License MITImported 8/10/2026Version v1

Skill contents

SKILL.md

CI/CD Pipeline Generator Skill

Overview

This skill generates CI/CD pipelines for SDK build, test, and release workflows across multiple languages and platforms.

Capabilities

Generate GitHub Actions workflows
Generate GitLab CI/CD pipelines
Configure multi-language build matrices
Set up release automation
Implement quality gates and checks
Configure artifact publishing
Support monorepo workflows
Implement security scanning

Target Processes

SDK Versioning and Release Management
SDK Code Generation Pipeline
SDK Testing Strategy

Integration Points

GitHub Actions
GitLab CI/CD
CircleCI
Jenkins
Azure Pipelines

Input Requirements

Languages to build
Testing requirements
Release workflow
Publishing targets
Quality gate requirements

Output Artifacts

CI/CD workflow files
Build configurations
Release automation scripts
Quality gate definitions
Publishing configurations
Security scan integration

Usage Example

yaml
skill:
  name: cicd-pipeline-generator
  context:
    platform: github-actions
    languages:
      - typescript
      - python
      - go
    stages:
      - lint
      - test
      - build
      - publish
    qualityGates:
      coverage: 80%
      linting: required
      security: required
    release:
      trigger: tags
      semantic: true
    publishing:
      npm: true
      pypi: true
      goModule: true

Best Practices

Use matrix builds for efficiency
Implement proper quality gates
Automate releases with semantic versioning
Include security scanning
Cache dependencies appropriately
Use reusable workflow components

Illustrative Want to see what a5c-ai/cicd-pipeline-generator would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/30/2026, 09:36 UTC · 25 cases

Lift

+16%

92% vs 76%

Agent turns

0%

1.0 vs 1.0

Tokens

+17%

3,278.36 vs 2,805.12

fail → pass · 4pass → pass · 19fail → fail · 2

Per-case results (25)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-01 We are setting up a GitHub Actions workflow to build our open-source SDK across Python, Go, and TypeScript. Developers usually run individual sequential jobs per language, but we want to optimize execution time. How should the pipeline configuration structure these language builds in GitHub Actions?	fail→pass	16,891	23,976	+42%	1	1	0%	3,219	4,160	+29%	0	0	—
case-02 We are configuring CI quality gates for an SDK repo and debating test code coverage enforcement. Team members suggest setting an aggressive 95% threshold or skipping strict gate blocking on PRs. What coverage threshold and quality gate configuration standard should be established for the CI test stage?	fail→fail	20,577	22,481	+9%	1	1	0%	2,457	2,894	+18%	0	0	—
case-03 Our SDK team wants to automate package publishing whenever changes are merged into the main branch. However, releasing on every main branch commit causes pre-release clutter. How should the CI/CD pipeline trigger and handle automated production release workflows?	pass→pass	17,050	15,006	-12%	1	1	0%	2,757	3,048	+11%	0	0	—
case-04 We publish our SDK to NPM, PyPI, and Go module mirrors. Our current process manually triggers separate platform publish scripts post-build. How should the publishing stage be structured across targets in the pipeline configuration?	pass→pass	22,624	25,590	+13%	1	1	0%	3,031	3,605	+19%	0	0	—
case-05 When building automated pipeline configurations for SDKs, developers often defer vulnerability checks to weekly scheduled scans or external dashboards to speed up PR build times. How should security scanning be integrated into the CI workflow execution stages?	pass→pass	22,368	23,711	+6%	1	1	0%	2,836	3,503	+24%	0	0	—
case-06 We are designing a pipeline stage sequence for an SDK repository containing client libraries across multiple languages. What is the standard ordering of pipeline stages from initial validation to final package distribution?	fail→fail	22,810	21,461	-6%	1	1	0%	2,926	3,301	+13%	0	0	—
case-07 We are writing a `.gitlab-ci.yml` pipeline for a multi-language SDK project across TypeScript and Python. Should we write duplicate pipeline jobs for each language environment?	pass→pass	18,090	19,940	+10%	1	1	0%	2,317	3,310	+43%	0	0	—
case-08 Our organization maintains 15 SDK repositories, and each repo currently duplicates identical GitHub Actions YAML definitions for testing and publishing. How should we structure these workflows to maintain consistency across all SDK repos?	pass→pass	14,408	22,760	+58%	1	1	0%	2,601	3,728	+43%	0	0	—
case-09 In our Python and TypeScript SDK build pipelines, dependency installation takes most of the execution time on clean GitHub-hosted runners. What optimization pattern should be integrated into the workflow steps?	pass→pass	18,437	18,328	-1%	1	1	0%	2,507	2,842	+13%	0	0	—
case-10 What are the core target processes that a comprehensive SDK CI/CD pipeline generator framework governs when managing SDK repositories?	fail→pass	22,193	18,845	-15%	1	1	0%	2,838	2,739	-3%	0	0	—
case-11 An enterprise architecture team is surveying supported CI/CD orchestrator platforms for standardized SDK delivery pipelines. Which major integration platforms should be supported in the pipeline architecture?	pass→pass	16,591	14,312	-14%	1	1	0%	2,678	2,644	-1%	0	0	—
case-12 When designing an automated generator tool for SDK CI/CD pipelines, what core input parameters must be collected from the user before generating workflow files?	pass→pass	20,878	19,143	-8%	1	1	0%	2,699	2,969	+10%	0	0	—
case-13 What set of configuration files and scripts should an SDK pipeline generator yield as its output artifacts when run?	pass→pass	16,140	14,199	-12%	1	1	0%	2,815	3,061	+9%	0	0	—
case-14 Show the structured YAML configuration schema used to define context for a CI/CD pipeline generator skill operating on GitHub Actions with TypeScript, Python, and Go across lint, test, build, and publish stages.	fail→pass	18,799	12,463	-34%	1	1	0%	3,849	3,166	-18%	0	0	—
case-15 In an automated SDK release pipeline context configuration, we want to specify quality gate constraints for linting, unit test coverage, and security vulnerabilities. How should these gates be defined in the context object?	fail→pass	15,638	17,248	+10%	1	1	0%	2,880	2,707	-6%	0	0	—
case-16 We are configuring the release phase of our Python SDK in GitHub Actions. We want to publish built wheels automatically to PyPI upon tagging. What publishing configuration standard should be specified?	pass→pass	13,943	16,828	+21%	1	1	0%	1,618	2,718	+68%	0	0	—
case-17 For a TypeScript SDK pipeline, how should package publication to the NPM registry be specified in the publishing context of the pipeline?	pass→pass	18,257	15,995	-12%	1	1	0%	2,460	2,317	-6%	0	0	—
case-18 Unlike NPM or PyPI, Go modules do not require uploading built binaries to a central binary artifact repository. How is Go module publishing specified in the pipeline context?	pass→pass	19,611	16,739	-15%	1	1	0%	2,504	2,483	-1%	0	0	—
case-19 Which type of security scanning step should be embedded in the SDK pipeline to catch both open-source dependency vulnerabilities and code flaws prior to artifact release?	pass→pass	12,150	14,840	+22%	1	1	0%	1,234	2,161	+75%	0	0	—
case-20 Our team uses OpenAPI specs to generate multi-language SDKs automatically. How does the CI/CD pipeline integrate code generation into its build and verification workflow?	pass→pass	22,890	25,612	+12%	1	1	0%	2,940	4,191	+43%	0	0	—
case-21 We store 4 language SDKs in a single repository root. How should the pipeline handle triggers so editing the Python SDK does not re-build and re-publish the Go SDK package?	pass→pass	18,516	14,655	-21%	1	1	0%	2,348	3,249	+38%	0	0	—
case-22 What are the fundamental best practices when architecting CI/CD pipelines for multi-language SDK delivery?	pass→pass	24,135	21,621	-10%	1	1	0%	3,084	3,779	+23%	0	0	—
case-23 We need a Dockerfile for a Python FastAPI microservice to run in Kubernetes. Can you write a multi-stage Dockerfile using python:3.11-slim with gunicorn and uvicorn workers?	pass→pass	19,247	18,873	-2%	1	1	0%	2,999	3,245	+8%	0	0	—
case-24 Please write a Terraform module using the official AWS provider to provision an EKS cluster with managed node groups for running our CI runner fleet.	pass→pass	25,948	37,258	+44%	1	1	0%	5,574	7,462	+34%	0	0	—
case-25 Here is a Python SDK class UserClient with a method get_user(user_id: str). Write a Pytest unit test file with mocked HTTP responses using unittest.mock.	pass→pass	13,229	15,628	+18%	1	1	0%	2,957	2,677	-9%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +16 percentage points is the difference between those two pass rates over the 25 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 76%
Pass rate with the skill: 92%
Cases attempted: 25
Cases counted toward lift: 25
Cases that regressed: 0
Skill version measured: v1
Run completed: 8/30/2026, 09:36 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-01

Prompt

We are setting up a GitHub Actions workflow to build our open-source SDK across Python, Go, and TypeScript. Developers usually run individual sequential jobs per language, but we want to optimize execution time. How should the pipeline configuration structure these language builds in GitHub Actions?

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run92

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+21% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 30 days ago

Other measured skills in the registry, with their headline benchmark lift.