harrey401
harrey401 publishes 7 tracked skills in DriftBot.
π Runtime quality summary
This is the quality surface for the publisher, not just a directory listing. It shows how much of the catalog has real receipts, how often those receipts are passing, whether richer fixture-backed proof exists, and whether the publisher currently carries regressions, reproduced failures, or stale runtime evidence.
Latest runtime touch: n/a. Publisher-level summaries do not replace skill-level review, but they do make reputation more earned: a publisher with broader coverage, stronger pass rates, and fixture-backed proof looks different from one living on thin smoke tests.
If you want the system-wide view, open the runtime dashboard. If you want the scoring logic, read the methodology.
No runtime receipts for this publisher yet.
Skills from this publisher
Label mix on this page
This distribution is a quick provenance cue, not a verdict. A publisher can have a mix of safer and riskier skills, so the useful move is to compare patterns here and then open the individual scorecards.
Publisher profiles are best for spotting catalog patterns: repeated shell access, common external services, whether manual review exists, and whether higher-risk labels are isolated or widespread.
On this page: 7 source-scanned, 0 catalog-only, and 0 manually reviewed entries in the current slice.
If you want the scoring logic, read the methodology. If you want the broader landscape, go back to the full index.
brain-cms
Continuum Memory System (CMS) for OpenClaw agents. Replaces flat MEMORY.md with a brain-inspired multi-layer memory architecture β semantic schemas, a hippocampal router (INDEX.md), vector store (LanceDB + nomic-embed-text), and automated NREM/REM sleep cycles for consolidation. Based on neuroscience research (LTP, spreading activation, CMS theory). Use when setting up persistent agent memory, improving context efficiency, or reducing token cost on long-running agents. Triggers: brain, memory system, CMS, long-term memory, vector store, sleep cycle, NREM, REM, memory architecture, semantic memory, context efficiency.
+ 1 more
lofy-fitness
Fitness accountability for the Lofy AI assistant β workout logging from natural language, meal tracking with calorie/protein estimates, PR detection with Epley formula, gym reminders based on weekly targets, and progress reports. Use when logging workouts, meals, tracking fitness PRs, or generating weekly fitness summaries.
+ 1 more
lofy-life-coach
Personal accountability system for the Lofy AI assistant β morning briefings, evening reviews, weekly reports, goal tracking, habit monitoring with streak counting, and adaptive nudge logic. Use when managing daily routines, life goals, habit streaks, or delivering scheduled briefings and reviews.
+ 1 more
lofy-career
Job search automation for the Lofy AI assistant β application tracking, resume tailoring to job descriptions, interview prep with company research, follow-up management with draft emails, and pipeline analytics. Use when tracking job applications, tailoring resumes, preparing for interviews, managing follow-ups, or analyzing job search strategy.
+ 1 more
lofy-projects
Project management for the Lofy AI assistant β tracks multiple projects with milestones, priority scoring engine (urgency Γ job relevance Γ momentum Γ energy match), meeting prep automation, time logging, stale project alerts, and work session recommendations. Use when managing projects, prioritizing work, preparing for meetings, or tracking milestones and deadlines.
+ 1 more
lofy
Personal AI chief of staff β a complete life management system for OpenClaw. Proactive morning briefings, evening reviews, weekly reports, fitness tracking, career management, project tracking, smart home control, and brain-inspired memory architecture. Use when setting up a personal AI assistant that manages your entire life through natural conversation across Telegram, WhatsApp, Discord, or any OpenClaw channel.
+ 1 more
lofy-home
Smart home control for the Lofy AI assistant β scene modes (study, chill, sleep, morning, grind), device management via Home Assistant REST API, presence-based automation, natural language commands for lights, music, thermostat, and PC wake-on-LAN. Use when controlling smart home devices, activating scene modes, or managing home automation.
+ 1 more
Trust reading guide
Publisher-level summaries help with provenance context, but trust still lives at the skill level. Use this page to compare patterns across the publisherβs catalog, then inspect the raw findings on individual skill pages.