pskoett/pskoett-ai-skills▌

The agent rides the wave of peak context. When the wave crests, it commits. When it detects drift, it pulls out cleanly — saving state, handing off, and letting the next session catch the next wave.

Query the DX Data Cloud PostgreSQL database using the mcp__dx-mcp-server__queryData tool.

Run a CI-only variant of Simplify & Harden in pull requests:

This skill turns implicit intent into an explicit, trackable artifact at the moment execution starts. It creates a lightweight intent contract, watches for scope drift while work is in progress, and closes each intent with a short resolution record.

For CI-only execution, use:

Every skill in this collection is built around a core philosophy — a principle that agents struggle to internalize on their own.

Automated learning capture in CI pipelines that deduplicates failure patterns and proposes prevention rules. \n \n Inspects PR check results and CI failures to identify recurring patterns tracked by stable pattern_key , promoting only when recurrence thresholds are met (3+ occurrences across 2+ distinct runs within 30 days) \n Ingests learning candidates from simplify-and-harden-ci and emits machine-readable YAML output without interactive prompts, suitable for headless GitHub Actions workflows

A two-phase team loop that produces production-quality code: implement, then audit using simplify + harden passes, then fix audit findings, then re-audit, repeating until the codebase is solid or the loop cap is reached.

Captures learnings, errors, and corrections to enable continuous improvement across agent tasks. \n \n Logs failures, user corrections, knowledge gaps, and API errors to structured markdown files ( .learnings/ERRORS.md , LEARNINGS.md , FEATURE_REQUESTS.md ) with consistent ID, priority, and metadata \n Supports promotion of broadly applicable learnings to project memory files ( CLAUDE.md , AGENTS.md , .github/copilot-instructions.md ) to prevent recurring mistakes \n Includes recurring pattern d