phenomenoner

Govern memory, episode, skill, and fact lifecycle maintenance through the unified curate scan, review, apply, verify, and rollback verbs.

Operate graph readiness, topology lookup, synthesis-aware routing, and symbolic evidence canvases. Use for project ownership, impact, dependency, or idea-to-project questions.

Govern embedding refresh, episodic evidence synchronization, metadata writeback, and engine dataset snapshots. Use before or after bounded sync, reindex, migration, or writeback operations.

Govern source-linked temporal facts in openclaw-mem. Use for current truth, timelines, stale detection, fact packs, and review-only fact extraction.

Operate experimental Dream Lite planning, governed refresh canaries, rollback, and Director rehearsals. Use only for explicitly reviewed derived card maintenance.

Operate the experimental GBrain lookup, restricted helper-job, and governed refresh-canary lanes. Use only when GBrain integration is explicitly enabled.

Operate the experimental read-only goal primitive and surface validation. Use for normalized goal status, compact goal packs, and receipt validation, not continuation or autonomous mutation.

Inspect experimental self-improvement consolidation surfaces. Use for read-only validation, goal readback, staged skill proposals, curator review, and system status.

Use when adding, checking, or troubleshooting a local Qdrant recall sidecar for Hermes Agent skills or recent sessions. Prefer local-first indexing, dry-run previews, and privacy-preserving defaults.

Use when a Hermes Agent task becomes long, tool-heavy, evidence-heavy, or likely to lose context. Maintain a compact Task Canvas with raw evidence refs instead of carrying every log in active context.

Fubon Neo (富邦新一代/富邦 API) Python SDK guidance focused on trading and market data workflows, including login, market data access, order placement, and locating the right docs/llms outputs. Use when prompts mention FubonNeo API or the Python SDK.

Optimize an OpenClaw agent setup (model routing, context management, delegation, rules, memory). Use when asked about optimizing agents, improving OpenClaw setup, agent best practices, heartbeat/cost guardrails, or choosing optimization priorities across cost/quality/reliability.

Operate and diagnose the A2A Superhub product across its CLI, HTTP, and declared MCP/A2A surfaces. Use when the user explicitly asks to inspect a Superhub, create or read Superhub tasks or artifacts, work with Superhub memory/inbox/wakeup/handoffs, validate this product skill, or diagnose Superhub auth, compatibility, index, queue, or capability state. Do not trigger for generic A2A protocol education, generic agent-memory design, vector database comparisons, repository summaries, or unrelated products with a similar name.

Install and operate a review-only molt-gic autopacket loop for an agent artifact. Use when an operator wants automatic self-improvement packets from receipts/digests without automatic apply or runtime config mutation.

Audit and slim OpenClaw prompt context to prevent context overflow and reduce cost. Use when the user says /context-clean-up, or asks to reduce prompt bloat, shrink session history, tame noisy cron or heartbeat output, or investigate a Context overflow error.