How to use dsh-okf-memory
OKF v0.1 knowledge memory driven by cognitive-neuroscience principles: predictive recall (predict-then-verify), uncertainty-driven exploration, prediction-error capture (user corrections / first disclosures trigger writes), and a reinforcement feedback loop (score = relevance x weight x recency) with weight decay and archiving. TechChoice type distills frontend/backend/language selections into candidate tables with a three-tier choice rule; every concept is standard OKF Markdown (type-required frontmatter, index/log, cross-links).
This article is auto-derived from indexed fields (wiki / faq / compatibility_json), not freshly AI-generated.
This article is derived from the plugin's already-indexed fields (wiki / faq / compatibility_json / readme), not freshly generated by AI. Source field is noted at the end of each section.
Quick start
OKF v0.1 knowledge memory driven by cognitive-neuroscience principles: predictive recall (predict-then-verify), uncertainty-driven exploration, prediction-error capture (user corrections / first disclosures trigger writes), and a reinforcement feedback loop (score = relevance x weight x recency) with weight decay and archiving. TechChoice type distills frontend/backend/language selections into candidate tables with a three-tier choice rule; every concept is standard OKF Markdown (type-required frontmatter, index/log, cross-links).
— source: plugins.ai_summary
Install & verify
dsh plugin --profile web add github:ZHI-QI/dsh-okf-memory
Run the command above in your DSH Web Profile. Then enable the plugin in the plugin list.
— source: plugins.install
Pitfalls
Review the upstream repo before installing. This guide is auto-derived from indexed fields and may lag the latest release. If anything contradicts the official docs, treat the upstream source as authoritative.
— source: general rule