Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.
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See how to installReview the instructions the agent will receive before you install.
Use this skill when an agent must rescue a pull request.
The goal is not to produce a long review. The goal is to decide whether the PR is safe, find the most important problem if it is not, and identify the smallest fix and test.
For every changed workflow, ask:
Return only actionable review or rescue output.
For each finding:
If applying a patch, keep it minimal and explain what changed.
If no issues are found, say that directly and list the remaining test gaps or residual risk.
After each run, inspect the score and feedback.
If the run failed, update this skill with one concrete rule that would have prevented the failure.
Prefer targeted improvements:
Avoid vague improvements:
Keep the skill short enough that another agent can use it quickly.
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Use at a cognee hackathon. Watches the participant's experience with cognee throughout the session — errors, slowness, confusion, workarounds, and signs of frustration in how they work with you — and keeps one private feedback file they send to the cognee team at the end.
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Use at the Mem Off hackathon. Keeps a running, evidence-backed report on how the participant's memory system performed for their coding agent during the day — what the harness measured, what cognee-report's scorecard says, what was recalled and whether it helped — in ./memoff-report.md and ./memoff-report.json. The report is submitted together with the action results; it never contains quotes, keys, data or names.
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Build spend analytics and dashboards from Qdrant vector data. Covers bulk scroll extraction, aggregation patterns, vendor grouping, and Chart.js visualization. Use when building analytics features, aggregating procurement data, or creating dashboards.
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Detect anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.
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Semantic search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.
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