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-ai --skill optimize-pipelinegit clone --depth 1 https://github.com/harness/harness-aiWrote 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-ai/optimize-pipeline)<a href="https://agentmods.dev/skills/harness/harness-ai/optimize-pipeline"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/optimize-pipeline.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00146 | $0.01385 |
| Opus 5 | $0.00073 | $0.00692 |
| Sonnet 5 | $0.00029 | $0.00277 |
| Haiku 4.5 | $0.00015 | $0.00138 |
Grade A, and why
optimize-pipeline scanned grade A with 0 findings 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 8d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to optimize-pipeline — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Pipeline
Analyze and optimize Harness CI/CD pipeline performance through parallel testing, caching, bottleneck analysis, and monorepo strategies.
Instructions
Step 1: Establish Scope
Confirm the service, pipeline, and current performance baseline.
Call MCP tool: harness_list
Parameters:
resource_type: "pipeline"
org_id: "<organization>"
project_id: "<project>"
Get recent execution timing data:
Call MCP tool: harness_list
Parameters:
resource_type: "execution"
org_id: "<organization>"
project_id: "<project>"
pipeline_id: "<pipeline_identifier>"
Step 2: Identify the Optimization Task
Determine which optimization the user needs:
- Parallel Testing with Test Intelligence -- Split tests across runners and skip unchanged tests
- Caching Strategy -- Multi-layer dependency, build output, and test result caching
- Pipeline Bottleneck Analysis -- Stage-level timing breakdown with recommendations
- Cache Hit Rate Improvement -- Diagnose and fix low cache hit rates
- Monorepo CI Pipeline -- Selective builds triggered by changed paths
Step 3: Configure Parallel Testing with Test Intelligence
Gather from the user:
- Test framework (JUnit, pytest, Jest, Go test, etc.)
- Total test count and current runtime
- Target runtime
Design the parallel test strategy:
- Split tests across N parallel runners using Harness Test Intelligence
- Use TI to identify and skip unchanged tests based on code changes
- Configure test splitting method: by class, by file, or by timing data
- Set up test result aggregation across runners
- Track TI savings over time (tests skipped vs. total)
Configuration:
- Enable Test Intelligence in the pipeline stage
- Set parallelism level based on test count and runner capacity
- Configure test report collection from all parallel runners
- Set up failure thresholds (e.g., fail the stage if any runner fails)
Step 4: Design Caching Strategy
Gather from the user:
- Build tool (Maven, Gradle, npm, yarn, pip, Go modules)
- Current build time breakdown (dependency download, compile, test)
- Cache key source (lockfile hash, manifest hash)
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.
- 8d ago First seen · 170 lines · 146 tokens per session scan A db6d25d9c2a9
optimize-pipeline is a skill published in the GitHub repository harness/harness-ai (19 stars, last pushed 17d ago), licensed Apache-2.0. It adds 146 tokens to every session and 1,385 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to optimize-pipeline, differing in 0 lines, and is treated as a copy.
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