Facilitate structured retrospectives across six dew development workflow stages.
npx skills add https://github.com/jkerdels/dew-plugin --skill dew-debrief
Please help me install this Agent Skill. Skill: dew-debrief Source: https://github.com/jkerdels/dew-plugin/tree/main/skills/dew-debrief Command: npx skills add https://github.com/jkerdels/dew-plugin --skill dew-debrief
This skill helps teams conduct a rigorous post-mortem of a completed dew development cycle, turning observations into specific, actionable configuration changes for the next run.
SKILL.md files, so improvements persist into future cycles.Use the dew-debrief skill after a completed Discover → Design → Demonstrate → Develop → Document cycle to generate a debrief and write confirmed lessons back into the participating skill configurations.
High-intent search queries and answers about installing and using this skill.
Root cause analysis for development friction is facilitated by a structured retrospective across six workflow stages, validating likely causes like skill prompts or tooling with falsifiable hypotheses. It reads stage artifacts and optionally reviews DAG evidence to quantify backtracks.
Writing retrospective lessons into SKILL.md files institutionalizes knowledge by recording confirmed findings directly into participating skill configurations. This persists improvements into future cycles by updating the configuration with specific, actionable changes.
A structured retrospective is needed after a completed Discover, Design, Demonstrate, Develop, and Document cycle where the team requires causal analysis of handoff failures. It guides collaborative inquiry to surface what worked and what didn't across all stages.
DAG evidence can be optionally reviewed to quantify completion, backtracks, invalidations, stage alignment, and remaining nodes. It provides dependency graph data to support the retrospective and validate root causes of workflow friction.
This retrospective process requires existing Discover, Design, Demonstrate, Develop, and Document artifacts to function correctly. It reads state and stage artifacts to synthesize skill-specific findings and write confirmed lessons back into the configurations.
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