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ai-operations

Configure Harness AI-powered operations (AIDA) via MCP. Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation. Configure intelligent alert correlation and noise reduction to reduce alert volume. Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection. Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead). Trigger phrases: AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting.

Quality

84%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

61%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, well-scoped configuration skill whose main weakness is that it stops at requirements gathering: it lists the exact parameters to collect and outcomes to configure, but provides almost no executable MCP invocations for the core configuration steps and no verification checkpoints. Conciseness and organization are good, with only minor duplication.

Suggestions

Show the actual MCP tool calls (tool name plus parameters) for the core tasks — configuring a failure prediction scenario and a correlation rule — as is done for harness_list, so the skill is copy-paste executable rather than a requirements checklist.

Add validation checkpoints after each configuration step (e.g., re-query the configured resource to confirm it was applied, or trigger a test prediction/alert) to close the workflow-clarity gap.

Consolidate the duplicated data-source enumerations in Step 3 (the 'Gather from the user' list and the 'Configure data sources' list repeat the same metrics/log/trace sources) into a single place.

DimensionReasoningScore

Conciseness

The body is dominated by efficient, parameter-style lists with no explanations of concepts Claude already knows, but there is duplication — data sources ('Datadog, Prometheus, CloudWatch') are enumerated in Step 3's gather list and again under 'Configure data sources' — that could be trimmed.

4 / 5

Actionability

Only one MCP call is shown (harness_list, for scope establishment); the core steps ('Configure failure prediction scenarios:', 'Configure alert correlation:') state what to configure with concrete parameter options but never give the actual MCP tool calls, parameters, or commands to perform the configuration. The guidance is specific yet missing the executable 'how' for the main tasks.

3 / 5

Workflow Clarity

Steps 1-4 are clearly sequenced (scope, task identification, then the two configuration workflows), but there are no validation checkpoints — nothing tells Claude to verify the configuration was applied or test that predictions/alerts fire. The Troubleshooting section is reactive issue guidance, not inline checkpoints, so the sequence matches anchor 3 rather than 4.

3 / 5

Progressive Disclosure

No bundle files exist and the skill is a single self-contained ~106-line file with well-organized sections (Instructions, Examples, Performance Notes, Troubleshooting) and no nested references. Minor gap: the troubleshooting and performance-notes sections could arguably live in a separate reference file to keep the main instructions leaner.

4 / 5

Total

14

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: concrete capabilities, comprehensive natural-language triggers, explicit use-when guidance, and negative boundary disambiguation against neighboring skills. Third-person voice is used throughout and nothing is padded or over-claimed.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — 'Set up predictive failure analysis with ML models for memory leaks, disk exhaustion, connection pool saturation, and latency degradation' and 'Configure intelligent alert correlation and noise reduction to reduce alert volume' — giving comprehensive coverage of the skill's capabilities with no meaningful gaps.

5 / 5

Completeness

It explicitly answers both 'what' (predictive failure analysis and alert correlation/noise reduction configuration) and 'when' ('Use when asked to set up predictive failure analysis, configure AI-powered alerting, reduce alert noise, or enable ML-based anomaly detection') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Beyond the 'Use when asked to...' clause, it enumerates natural trigger phrases ('AIDA, predictive failure, alert correlation, noise reduction, anomaly detection, AI ops, predictive analysis, alert fatigue, ML alerting, intelligent alerting') covering synonyms and colloquial variations a user would actually say.

5 / 5

Distinctiveness Conflict Risk

It occupies a clear niche (Harness AIDA predictive operations) and explicitly disambiguates adjacent skills: 'Do NOT use for pipeline debugging (use debug-pipeline instead) or SLO management (use manage-slos instead)', minimizing conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

harness/harness-ai

Path

plugins/claude/skills/ai-operations/SKILL.md

Reviewed

Table of Contents

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