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 ai-operationsgit 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/ai-operations)<a href="https://agentmods.dev/skills/harness/harness-ai/ai-operations"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/ai-operations/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/ai-operations"><img src="https://agentmods.dev/badge/skills/harness/harness-ai/ai-operations.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 | $0.00137 | $0.01180 |
| Opus 5 | $0.00068 | $0.00590 |
| Sonnet 5 | $0.00027 | $0.00236 |
| Haiku 4.5 | $0.00014 | $0.00118 |
Grade A, and why
ai-operations 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 9d 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 ai-operations — 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Operations
Configure AI-powered predictive failure analysis and intelligent alert correlation using Harness AIDA.
Instructions
Step 1: Establish Scope
Confirm the user's org, project, service, and observability stack.
Call MCP tool: harness_list
Parameters:
resource_type: "project"
org_id: "<organization>"
Step 2: Identify the AI Operations Task
Determine which workflow the user needs:
- Predictive Failure Analysis -- ML-based detection of impending failures before SLO breach
- Alert Correlation and Noise Reduction -- Group related alerts and suppress duplicates
Step 3: Configure Predictive Failure Analysis
Gather from the user:
- Service name and data sources (Datadog, Prometheus, CloudWatch)
- Prediction horizon (30 minutes, 1 hour, 4 hours, 24 hours ahead)
- Training data period (30 days, 90 days, 6 months)
- Model type preference (anomaly detection, time series forecasting, ensemble)
Configure failure prediction scenarios:
- Memory leak detection -- Flag services where memory grows above threshold per window
- Disk exhaustion -- Predict time-to-full and alert N hours in advance
- Connection pool saturation -- Alert when pool usage exceeds threshold for sustained duration
- Latency degradation -- Detect progressive slowdown before SLO breach
- Deployment-induced regression -- Correlate metric changes with recent deployments
Configure alerting:
- Set prediction confidence threshold (suppress below threshold to reduce noise)
- Route alerts to PagerDuty, Slack, or other channels
- Enable auto-generated runbook suggestions using AIDA
- Set up false positive feedback loop for model improvement
Configure data sources:
- Metrics source (Prometheus, Datadog, CloudWatch)
- Log source (Elasticsearch, Splunk, CloudWatch Logs)
- Trace source (Jaeger, Datadog APM, AWS X-Ray)
- Model retraining frequency (daily, weekly, monthly, on data drift)
Step 4: Configure Alert Correlation and Noise Reduction
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.
- 9d ago First seen · 126 lines · 137 tokens per session scan A 8f8f87fd8091
ai-operations is a skill published in the GitHub repository harness/harness-ai (19 stars, last pushed 18d ago), licensed Apache-2.0. It adds 137 tokens to every session and 1,180 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 ai-operations, differing in 0 lines, and is treated as a copy.
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