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 run-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/run-pipeline)<a href="https://agentmods.dev/skills/harness/harness-ai/run-pipeline"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/run-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-ai/run-pipeline"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/run-pipeline.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00120 | $0.01450 |
| Opus 5 | $0.00060 | $0.00725 |
| Sonnet 5 | $0.00024 | $0.00290 |
| Haiku 4.5 | $0.00012 | $0.00145 |
Grade A, and why
run-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 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.
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 run-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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run Pipeline
Execute and monitor Harness pipeline runs via MCP.
Instructions
Step 1: Find the Pipeline
Call MCP tool: harness_search
Parameters:
query: "<user's pipeline name or keyword>"
resource_types: ["pipeline"]
compact: true
Or list all pipelines in a project:
Call MCP tool: harness_list
Parameters:
resource_type: "pipeline"
org_id: "<organization>"
project_id: "<project>"
search_term: "<optional filter>"
Step 2: Get Pipeline Details
Call MCP tool: harness_get
Parameters:
resource_type: "pipeline"
resource_id: "<pipeline_identifier>"
org_id: "<organization>"
project_id: "<project>"
Extract from response:
- Required runtime inputs (
<+input>fields) - Pipeline variables with types and defaults
- Stage structure and deployment targets
Step 3: Check Available Input Sets
Call MCP tool: harness_list
Parameters:
resource_type: "input_set"
org_id: "<organization>"
project_id: "<project>"
Input sets provide pre-configured values that can be used or overridden.
Step 4: Execute the Pipeline
Call MCP tool: harness_execute
Parameters:
resource_type: "pipeline"
action: "run"
resource_id: "<pipeline_identifier>"
org_id: "<organization>"
project_id: "<project>"
Step 5: Monitor Execution
Call MCP tool: harness_get
Parameters:
resource_type: "execution"
resource_id: "<execution_id>"
org_id: "<organization>"
project_id: "<project>"
Execution statuses: Running, Success, Failed, Aborted, Waiting (approval/input), Expired
Step 6: Handle Approvals
If execution is waiting for approval:
Call MCP tool: harness_list
Parameters:
resource_type: "approval_instance"
org_id: "<organization>"
project_id: "<project>"
Then approve or reject:
Call MCP tool: harness_execute
Parameters:
resource_type: "approval_instance"
action: "approve" # or "reject"
resource_id: "<approval_id>"
Safety: Production Deployments
CRITICAL: Before executing pipelines targeting production:
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 · 227 lines · 120 tokens per session scan A d635b7f9d3f7
run-pipeline is a skill published in the GitHub repository harness/harness-ai (19 stars, last pushed 19d ago), licensed Apache-2.0. It adds 120 tokens to every session and 1,450 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to run-pipeline, differing in 0 lines, and is treated as a copy.
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