Distill recurring operational findings into deterministic defenses with manifest records.
npx skills add https://github.com/voodootikigod/adlc --skill adlc-distill
Please help me install this Agent Skill. Skill: adlc-distill Source: https://github.com/voodootikigod/adlc/tree/main/plugins/adlc-codex/skills/adlc-distill Command: npx skills add https://github.com/voodootikigod/adlc --skill adlc-distill
Automates the distillation of repeated operational findings into deterministic defenses and keeps guidance fresh, reducing manual toil and knowledge drift.
Run the distill workflow to convert recent findings into updated, deterministic defenses and schedule maintenance.
High-intent search queries and answers about installing and using this skill.
Automating the distillation of recurring operational findings into deterministic defenses requires a coordinated maintenance pipeline. This workflow consolidates repeated issues into updated policies and artifacts, reducing manual toil while keeping cached guidance fresh through CI cron fallbacks and manifest entry records.
Rejection-mining is a process that extracts lessons from failed operational runs. By distilling these recurring findings into updated policies, it actively prevents knowledge drift and ensures future runs are guided by deterministic defenses.
Keeping cached guidance fresh across end-to-end maintenance pipelines is achieved through scheduled maintenance and CI cron fallback mechanisms. This ensures distilled guidance is continuously updated from recent findings without manual intervention.
Yes, gate-fuzzing and model-ratchet workflows can be coordinated within the same end-to-end distillation pipeline. The pipeline consolidates findings across these components to generate updated, deterministic defenses with full traceability.
The best way to record distillation results for audits is by using manifest entry records within the distillation workflow. This provides clear traceability by logging the distilled findings and the resulting updated deterministic artifacts.
You need lesson-foundry pipelines to establish a deterministic workflow for converting a backlog of repeated issues into updated artifacts. Without this distillation process, manual toil increases and operational knowledge drifts over time.
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