Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add harness/harness-skills --skill create-pipelinegit clone --depth 1 https://github.com/harness/harness-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/harness/harness-skills/create-pipeline)<a href="https://agentmods.dev/skills/harness/harness-skills/create-pipeline"><img src="https://agentmods.dev/badge/skills/harness/harness-skills/create-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/harness/harness-skills/create-pipeline"><img src="https://agentmods.dev/badge/skills/harness/harness-skills/create-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 38 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
- medium Data Exfiltration · line 45 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.04806 |
| Opus 5 | $0.00060 | $0.02403 |
| Sonnet 5 | $0.00024 | $0.00961 |
| Haiku 4.5 | $0.00012 | $0.00481 |
Grade A, and why
create-pipeline scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Ticketing → use `JiraCreate` / `ServiceNowCreate` (never `Run: curl`) Copies of this mod
1 near-identical copy found in the catalogue:
- create-pipeline — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 601 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Pipeline
Generate Harness v0 Pipeline YAML and optionally push to Harness via MCP.
Instructions
- Analyze codebase (if source code is available) - Scan the project to auto-detect language, build tools, test frameworks, containerization, deployment manifests, and target infrastructure. Use the detection tables and decision tree in
references/codebase-analysis.mdto determine:- Language and runtime version (package.json → Node.js, go.mod → Go, pom.xml → Java, etc.)
- Build commands and base images
- Test framework and report format (Jest → JUnit, pytest → JUnit XML, etc.)
- Linter and formatter (ESLint, Prettier, Ruff, etc.)
- Dockerfile presence and registry type (Docker Hub, ECR, GCR, ACR)
- Deployment manifests → Harness service/deployment type (k8s manifests → Kubernetes, Chart.yaml → NativeHelm, task-definition.json → ECS, serverless.yml → ServerlessAwsLambda)
- Existing CI/CD configs for migration (GitHub Actions, Jenkins, GitLab CI, etc.)
- Clarify requirements - Confirm detected settings with the user. Ask about anything that couldn't be auto-detected — do not guess or use placeholders. If the user's request is ambiguous, ask before generating YAML. Examples of what to ask when missing:
- Deployment target / infrastructure: region (e.g. us-east-1), cluster name or ID, account ID (e.g. AWS account for ECR/ECS)
- Registry: which registry (Docker Hub, ECR, GCR, ACR), registry identifier/URL, repo path
- Cloud provider: which account, region, and resource identifiers for connectors/infrastructure
- Approval gates, notification channels if relevant
Critical rule: Never hardcode placeholder values (e.g.
123456789012,us-east-1,my-cluster) for deployment target, region, registry, or cluster when the user did not specify them — ask the user instead. If the user did not specify region, account ID, cluster, or registry (e.g. "deploys to ECS" with no region or cluster), ask the user for those values before generating YAML.
- Select native steps - Always prefer Harness native steps over
RunorShellScriptsteps. Consultreferences/native-steps.mdfor the full mapping. Key rules:- Docker build/push → use
BuildAndPushDockerRegistry/BuildAndPushECR/BuildAndPushGCR/BuildAndPushACR(neverRun: docker build && docker push) - K8s deploy → use
K8sRollingDeploy/K8sBlueGreenDeploy/K8sCanaryDeploy(neverRun: kubectl apply) - Helm deploy → use
HelmDeploy(neverRun: helm upgrade --install) - ECS deploy → use
EcsRollingDeploy(neverRun: aws ecs update-service) - Terraform → use
TerraformPlan/TerraformApply(neverRun: terraform apply) - Security scanning → use native STO steps (
AquaTrivy,Snyk,Sonarqube,Semgrep, etc.) - Uploads → use
S3Upload/GCSUpload(neverRun: aws s3 cp) - Approvals → use
HarnessApproval/JiraApproval(never polling scripts) - Ticketing → use
JiraCreate/ServiceNowCreate(neverRun: curl) - Use
Runsteps only for custom build/test/lint commands with no native equivalent - Test steps: Any Run step that runs unit or integration tests must include a
reportsblock (e.g.type: JUnit,spec.paths) so Harness can capture results; seereferences/codebase-analysis.mdfor framework → report path.
- Docker build/push → use
- Generate valid YAML following the structure below, using the detected build/test/deploy commands. Validation rules: (a) Stage names must match
^[a-zA-Z_0-9-.][-0-9a-zA-Z_\\s.]{0,127}$— use only letters, numbers, spaces, hyphens, underscores, or periods (no commas). (b) Every CI and CD stage must include afailureStrategiesarray (Approval stages do not require one). For CI useMarkAsFailure(neverIgnore— it hides failures); for CD useStageRollback. - Optionally create via MCP — First verify the project exists (see "Creating via MCP" section below), then use
harness_createwith resource_typepipelineandbody: { yamlPipeline: "<YAML string>" }
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 601 lines · 119 tokens per session scan A fcecfa556c78
create-pipeline is a skill published in the GitHub repository harness/harness-skills (105 stars, last pushed today), licensed Apache-2.0. It adds 119 tokens to every session and 4,806 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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gitlab-ci-patterns
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airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
nx-workspace-patterns
Configure and optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.