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Evaluate Cognee itself from local telemetry: recall hit rate, latency, saves, injected context, tokens saved, a scorecard and grade, before vs after an update, and why a memory was or wasn't injected last turn.
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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
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Use GBrain to give OpenClaw or Hermes-style agents a local knowledge brain with markdown ingestion, entity links, hybrid search, scheduled consolidation, and MCP access.
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Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.
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Sync OpenClaw + ClawHub + Skills docs into a local mirror for the QMD memory backend.
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xUnit, Moq/NSubstitute, FluentAssertions, and coverlet patterns for any .NET project. Covers test structure ([Fact]/[Theory]/[InlineData]), mocking discipline, fluent assertions, integration test setup with WebApplicationFactory, and coverage measurement. Stack-agnostic — referenced by every .NET plugin in the marketplace. Use this skill to: - Write clear, maintainable unit tests with xUnit [Fact] and [Theory]. - Mock dependencies with Moq or NSubstitute without overusing mocks. - Write expressive assertions with FluentAssertions. - Measure coverage with coverlet and enforce a minimum threshold. Do NOT use this skill for: - ASP.NET Core-specific integration tests (WebApplicationFactory, HttpClient — those are in aspnet-core-plugin:aspnet-conventions). - EF Core in-memory or SQL Server LocalDB test patterns (aspnet-core-plugin:efcore-patterns). - C# language idioms — see csharp-foundation:csharp-conventions.
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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.
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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Web runtime package: uses memory in this order: connector-backed, project-pack, none. Never assumes subprocess access or unrestricted local files. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles.
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OpenTSC v2.1: Soul/Shell architecture for interpersonal intelligence. Local, offline-first, with event graphs, judgment engine (K7), self-creation (K8), three-layer attributes, calibrated predictions, 11 VSM professions, and a pluggable zvec memory index (semantic search, identity resolution, jieba segmentation, emotion scoring incl. batched+cached LLM) that runs with zero extra deps. Transforms implicit social judgment into explicit, queryable, auditable, evolving intelligence.
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Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file. Use this skill to extract slice, thread, or memory data from Android Perfetto traces using trace_processor.
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Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
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Post-exploitation workflow. Covers privilege escalation (Linux SUID/sudo/kernel, Windows UAC/service/token), persistence assessment, local enumeration, credential harvesting, and pivot preparation. Structured workflows for Linux and Windows targets using impacket, netexec, john, linpeas/winpeas, and standard Kali tools. Includes kernel exploit reference tables, GTFOBins exploitation chains, Potato attack selection, Docker/container escapes, DLL hijacking, SSH key harvesting, credential recovery from memory, and Windows token manipulation. Chains from /pentester or /credential-audit when access is obtained.
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Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.
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Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.
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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
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Persistent, adaptive vector memory for agents on a ruOS desktop — a Rust-native vector database with local semantic embeddings, HNSW retrieval, and graph relationships. Use to remember and recall across sessions with no database server or API key.
中文简介正在准备中。当前显示原文说明。
Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
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Inspect reviewed Grafana infrastructure evidence for host or fleet CPU usage, memory load, disk space, and system load. Use for server-health checks, capacity symptoms, resource saturation, disk pressure, cloud or local host comparisons, and bounded infrastructure triage.
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Comprehensive Mastra framework guide for building agents, workflows, tools, memory, workspaces, and storage with current APIs. Use for documentation lookup, API verification, TypeScript setup, common errors, migrations, and `mastra api` CLI tasks: inspect or call resources on local, Mastra platform, Trace Intelligence, or remote servers.
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Apply the local C++ coding conventions of the EMULATORS framework. Use when Codex adds, edits, reviews, or scaffolds C++ code in this repository, especially new classes, headers, cpp files, CORE/COMMODORE module code, emulator components, registers, chips, memory, sound, IO, commands, or InfoClass-derived objects.
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Validate and implement Sinclair ZX Spectrum 48K ROM-visible behavior in the EMULATORS repository, including reset and restart vectors, IM1/NMI handling, keyboard scanning and repeat, beeper and tape routines, screen/printer output, channels and streams, BASIC tokenization and execution, expression evaluation, memory movement, system variables, calculator stack, floating-point formats, ROM traps, localized ROM selection, and firmware/hardware timing contracts. Use when Codex reviews or edits ZX Spectrum ROM loading, KERNAL-style traps, keyboard, tape, screen, printer, BASIC files, tokens, system variables, startup, interrupts, or compatibility tests.
中文简介正在准备中。当前显示原文说明。
Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
中文简介正在准备中。当前显示原文说明。
Onboard a new machine as a receive-only follower on a multi-machine agent network: it reads the shared vault, canon, skills and memory, runs nothing heavy locally (no RAG, reindex or GPU), and never receives executable hooks from the hub. Checklist-driven, with a verify step at each stage. Triggers: "/follower-onboard <machine>".
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Collective research memory for AI coding agents — search prior research before hitting the web, save findings back to the shared cache, view karma stats. Use when the user asks a library/API/framework question, before running WebSearch/WebFetch, or after deep web research so the next agent doesn't repay the cost. Talks directly to the hosted MCP endpoint via JSON-RPC — no local MCP server required.
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