Qdrant
DatabaseCommonly used with
Skills using Qdrant (106)
hunt-rag-vector
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.
index
Reindexes KB for semantic search via vector store (Qdrant). Triggers: reindex KB, rebuild index, vector reindex, refresh embeddings.
omni-settings
Read and update global application settings: system prompts, thinking budget, IP filters, payload rules, combo defaults, and require-login configuration.
rag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
chroma
Embedding database for RAG and semantic search.
qdrant-clients-sdk
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
qdrant-hybrid-search
Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to combine results from multiple searches?' and 'combining multiple representations'. Also use for how a hybrid query is scoped: 'how is IDF scoped?', 'can one tenant's data contaminate another tenant's scoring?'
qdrant-search-strategies
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', or 'missing keyword matches'
rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
bim-cost-estimation-cwicr
Automated cost estimation from BIM models using DDC CWICR database (8 national bases, 78,228 positions). AI classification + vector search for accurate pricing.
cwicr-data-loader
Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.
codebase-exploration
Explore and understand codebases using SocratiCode semantic search, dependency graphs, and context artifacts. Use when exploring code, understanding architecture, finding functions/types, analysing dependencies, searching database schemas or API specs, or when socraticode/codebase_search tools are available. Activates when the user asks about code structure, wants to find where a feature lives, or needs to understand how code is organised.
qdrant-monitoring
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', 'set up alerts', 'cluster health check', or needs to set up Prometheus, Grafana, health checks, or log centralization. Also use when debugging production issues that require metric analysis.
qdrant-scaling
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput or QPS', 'CPU is pegged / can't keep up with the request rate', 'one query is slow / p99 or tail latency too high', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', 'need to add capacity', 'large limit / pagination / scroll is slow', or 'only recent data matters / expiring old vectors / retention window'.
qdrant-scaling-data-volume
Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
qdrant-search-quality
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'how to combine keyword and vector search / fusion / RRF / prefetch?', 'should I use reranking?', 'relevance feedback?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', or 'how to score recall@k?'. Also use when search quality degrades after quantization, model change, or data growth.
retrieval
Retrieval - vector DBs, embeddings, hybrid search, reranking.
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
qdrant-advisor
Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slowness, scaling and sharding decisions (node count, QPS, latency, multitenancy, vertical vs horizontal), poor or irrelevant search results, hybrid search and reranking, embedding-model migration, version upgrades and compatibility, monitoring and observability (Prometheus, Grafana, health checks, /metrics, /telemetry), deployment choices (local, Docker, self-hosted, Qdrant Cloud, embedded), or client-SDK questions (Python, TypeScript, Rust, Go, .NET, Java). Trigger especially when the context is clearly a Qdrant cluster, collection, or vector-search deployment. Always prefer this skill over answering from memory: it pulls current, authoritative guidance and only the relevant context.
qdrant-deployment-options
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
qdrant-edge
Guides building on Qdrant Edge, the embedded in-process shard. Use when someone asks 'how to sync Edge with the server', 'keep a local shard in sync with Qdrant Cloud', 'BM25 or keyword search on Edge', 'hybrid search on Edge', 'embeddings on device', 'Edge snapshots', 'apply a partial snapshot', 'why is my Edge search empty after inserts', or is writing custom sync, BM25, or fusion code against qdrant-edge. Also use when deciding what Edge ships built-in versus what you must implement.
qdrant-horizontal-scaling
Diagnoses and guides Qdrant horizontal scaling decisions. Use when someone asks 'vertical or horizontal?', 'how many nodes?', 'how many shards?', 'how to add nodes', 'resharding', 'data doesn't fit', or 'need more capacity'. Also use when data growth outpaces current deployment.
qdrant-hybrid-search-combining
Fusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone asks 'RRF or DBSF?', 'how to combine sparse and dense', 'how to combine scores from multiple searches?', 'custom fusion', or 'fusion is not producing good results'
qdrant-hybrid-search-prefetches
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to use?', or 'BM25 vs SPLADE?'
qdrant-minimize-latency
Guides Qdrant query latency optimization. Use when someone asks 'search is slow', 'how to reduce latency', 'p99 is too high', 'tail latency', 'single query too slow', 'how to make search faster', or 'latency spikes'.
qdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
qdrant-monitoring-debugging
Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.
qdrant-monitoring-setup
Guides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, and log centralization. Use when someone asks 'how to set up monitoring', 'Prometheus config', 'Grafana dashboard', 'health check endpoints', 'how to scrape Hybrid Cloud', 'what alerts to set', 'how to centralize logs', or 'audit logging'.
qdrant-multitenancy
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain country or region'. Also use when they describe a symptom: one customer's data is way bigger than the rest and slowing everyone down, or one tenant is hogging resources.
qdrant-relevance-feedback
Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative to reranking', 'using a more heavy/big embedding model for dense search but can't afford it', 'finding more relevant documents beyond the initial search pool', or 'feedback loops'. Also trigger when the user has a search quality problem due to a dense retriever being weak and is considering reranking as a solution — this API may be a better fit
qdrant-scaling-qps
Guides Qdrant query throughput (QPS) scaling. Use when someone asks 'how to increase QPS', 'need more throughput', 'queries per second too low', 'batch search', 'read replicas', or 'how to handle more concurrent queries'.
qdrant-search-quality-diagnosis
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', 'quality dropped after quantization', 'how to measure retrieval quality', 'build a golden set', 'ground truth dataset', or 'how to score recall@k'. Also use when search quality degrades without obvious changes.
qdrant-sizing
Sizes a Qdrant deployment before it is provisioned. Use when someone asks 'how much RAM do I need', 'how many nodes', 'how big should my cluster be', 'sizing', 'capacity planning', 'will N vectors fit', 'what instance type should I pick', or gives a vector count and dimensions and asks what to provision. Also use when an existing estimate needs checking before hardware or a cluster tier is bought.
qdrant-sliding-time-window
Guides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire old vectors', 'time-based data rotation', 'delete old data efficiently', 'social media feed search', 'news search', 'log search with retention', or 'how to keep only last N months of data'.
qdrant-tenant-scaling
Guides Qdrant multi-tenant scaling. Use when someone asks 'how to scale tenants', 'one collection per tenant?', 'tenant isolation', 'dedicated shards', or reports tenant performance issues. Also use when multi-tenant workloads outgrow shared infrastructure.
qdrant-version-upgrade
Covers upgrading Qdrant server and SDKs without interrupting availability or losing data integrity. Use when someone asks 'how do I upgrade Qdrant', 'can I jump from 1.15 to 1.18', 'rolling upgrade without downtime', 'do I upgrade the client or the server first', 'which SDK version matches my server', 'what should I check before upgrading', or 'something changed after we upgraded'.
qdrant-vertical-scaling
Guides Qdrant vertical scaling decisions. Use when someone asks 'how to scale up a node', 'need more RAM', 'upgrade node size', 'vertical scaling', 'resize cluster', 'scale up vs scale out', or when memory/CPU is insufficient on current nodes. Also use when someone wants to avoid the complexity of horizontal scaling.
qdrant-scaling-query-volume
Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.
ai-llm-integration-expert
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
context-retrieval
Retrieve relevant information from a knowledge base using semantic, keyword, or hybrid search to ground a query. Use when the task starts with a corpus or index that must be searched; use context-ranking when candidate chunks already exist and only need ordering.
onboarding
First-run onboarding — guides new users through Genesis setup on their first CC session. Configures user profile, essential API keys, Telegram, GitHub backup, and service verification. Triggered automatically when ~/.genesis/setup-complete is absent. Re-runnable by asking Genesis to "run setup" or "reconfigure [section]".
antinet-provenance
软件开发工程师与信息安全分析师在构建多智能体系统时，当需全链路可观测与安全审计，请用此技能。它自动收集操作日志，生成可追溯证据链与向量索引，开箱即用实现系统审计与精准回放，让Agent运行安全透明。
ai-engine-wordpress-mcp-server-and-ai-automation
AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.
provenance
收集系统全链路操作日志，生成可追溯的执行证据链与向量索引，是多智能体系统可观测性与安全审计的底座。
review-pr-comments
Fetch PR review comments via gh CLI, classify them (valid & fix / valid & skip / invalid), fix the valid ones, reply and resolve the threads, and fix any CI failures. Invoke only when the user types /review-pr-comments — never auto-trigger.
vector-databases
Vector database integration for embeddings and similarity search. Pinecone, Weaviate, Qdrant, ChromaDB, pgvector. Index management, metadata filtering, hybrid search, and production optimization. USE WHEN: user mentions "vector database", "embeddings", "similarity search", "Pinecone", "Weaviate", "Qdrant", "ChromaDB", "pgvector", "HNSW", "ANN" DO NOT USE FOR: LangChain integration - use `langchain`; RAG architecture - use `rag-patterns`; traditional databases - use database skills
sota-databases
State-of-the-art database engineering rules (2026) for designing, building, and auditing data layers. Covers engine selection, schema modeling, migrations, query and index craft, transactions and concurrency, reliability and scale, security, and vector/AI workloads. Use when designing a new data layer, writing or reviewing schemas/migrations/queries, debugging slow or contended database workloads, or auditing an existing database for correctness, performance, and security. Not for ETL or streaming data pipelines — use sota-data-engineering. Trigger keywords: database, SQL, Postgres, schema, migration, index, query, ORM, transaction, NoSQL, Redis, vector DB, pgvector, replication, partitioning, connection pool, RLS, EXPLAIN, deadlock, sharding, caching, SurrealDB, SurrealQL, Qdrant, multi-model, graph database.
search-infra
Design and operate full-text and vector search infrastructure — inverted-index engines (Elasticsearch/OpenSearch, Meilisearch, Typesense), analyzers and tokenization, relevance tuning (BM25, boosting, synonyms), faceting, and semantic/vector search (embeddings, ANN indexes, hybrid retrieval). Use when adding a search box, choosing a search engine, designing an index mapping and analyzer chain, tuning relevance, building autocomplete, deciding keyword vs vector vs hybrid retrieval, or keeping a search index in sync with the source database. Boundary vs db-design — db-design owns the durable transactional source of truth (normalized PostgreSQL schema, known-key indexes, migrations, ACID); this skill owns the derived denormalized search index built FROM that source for ranked free-text/semantic retrieval, where the engine is eventually-consistent, rebuildable, and never the system of record. Defers cache concerns to redis and RAG prompt assembly to llm-patterns.
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
agenticx-quickstart
AgenticX zero-to-hero quickstart guide. Use when the user wants to get started with AgenticX, create their first project, build their first agent, or run their first workflow. Covers installation, project scaffolding, agent creation, task execution, and CLI basics.
chroma
Embedding database for RAG and semantic search.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
ccc-data
complete data ecosystem — 8 skills in one. Data pipelines, SQL optimization, visualization, machine learning, data quality, analytics, reporting, and vector search.
ariadne
Mandatory long-term memory workflow for Codex, Claude Code, and MCP clients backed by the local Ariadne server (Qdrant + bge-m3). Always use at the start or resumption of substantive project work, after context compaction, when prior work or decisions may matter, and immediately after durable decisions, gotchas, completed reports, release/deployment results, or verified status. Also use for explicit remember, recall, or Ariadne requests.
install-agentic-memory-stack
Use when the user wants to install, reinstall, or rebuild the agentic memory stack for Claude Code — multi-tier persistent memory (mem0 + Qdrant + EmbeddingGemma on llama-swap + Codex-driven extraction), with an episodic/goals/open-questions sidecar and a DPAPI-isolated canonical key, on a Windows + WSL2 machine. Triggers on phrases like "install agentic memory", "set up the memory stack", "reinstall after format", "rebuild memory backend", "set up mem0 + qdrant + codex".
ai-native-development
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows (ReAct, multi-agent, Opus 4.5), LLM integration, prompt engineering, streaming, and cost optimization. Use when: building an AI feature, integrating an LLM, setting up vector search, or designing agent architectures. Triggers on: AI app, LLM integration, RAG, vector database, agentic, prompt engineering, AI-native, semantic search, multi-agent, embeddings pipeline, cost optimization
remember
Recall past work across Claude Code and Codex sessions. Finds decisions, research, fixes, and conversations by intent.
rag-debug
Diagnose RAG pipeline issues — check Qdrant collection, embedding provider, MinIO storage, and recent chunk uploads
cluster
Treat the homelab/micro-cluster as one schedulable resource — discover every reachable machine and spread cheap work across them (least-recently-used first) so idle boxes get the next task, recovering wasted local capacity instead of paying the cloud. Use to see cluster status, route a task to the idlest machine, or hand a task to a trusted agent on another machine. Also use when the user mentions homelab, cluster, idle machines, load balancing, or distributing work across machines.
kb-recall
從本地 RAG 知識庫（NAS Qdrant kb_hybrid_v2）檢索相關片段。當用戶問過往決策/筆記/專案背景/安裝步驟/架構/操作指南，或問「知識庫有冇講過X」「之前係點搞」「KB 查吓」「recall from KB」，或 session 開頭的「📚 本專案知識庫」清單顯示有相關條目時觸發。
nexus-memory
Persistent memory for Claude Code powered by Qdrant. Auto-Recall injects relevant memories before each prompt. Auto-Capture stores facts after each turn. Self-hosted, private, works alongside Hermes and OpenClaw with the same Qdrant collection. Configure via NEXUS_* environment variables. Use when the user asks to "remember", "recall", "search memory", or when project context from past sessions is needed.
security-audit
Run a structured security audit of Linux servers and self-hosted stacks, then harden what's found. Covers Ubuntu/Debian hardening (SSH, sudo, firewall, kernel sysctls, systemd isolation, patching), Docker and container escape paths, exposed datastores (Redis, Postgres, Qdrant, MongoDB), secrets handling, and AI agent risks such as webhook authentication, tool permissions, and prompt injection reaching real tool calls. Use whenever the user asks to audit, review, harden, or lock down a server, VPS, container stack, or agent deployment — and for narrower questions that are really audit questions, like "is my Redis exposed", "is this server safe", "чи безпечний мій сервер", "проведи аудит серверів", "закрий вразливості", or "why is this port open" — or when they paste a docker-compose.yml, sshd_config, or firewall ruleset and ask whether it looks right. Prefer this over ad-hoc checking, since it enforces read-only diagnostics, consistent severity ratings, and a repeatable report.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
cli
Route web search, source indexing, extraction, RAG, jobs, and watch tasks through the local Axon CLI.
company-directories
Use when extracting structured company lists from public or authorized directories into JSON, CSV, CRM-ready lists, or research tables.
competitive-intel
Monitor competitor pricing, features, changelogs, dashboards, and product changes with Axon. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.
dashboard-reporting
Use when the user needs dashboard reporting from authorized browser-accessible pages; use external browser automation for login/UI interaction and Axon for public scrape/screenshot captures or exported artifacts.
deep-research
Use when the user explicitly needs a rigorous, cited, formal research report on a complex scientific, technical, policy, or market topic.
demo-walkthrough
Use when the user needs a product walkthrough from authorized browser automation, with Axon used for public page scrape/screenshot evidence or captured URLs.
extract
Use Axon extract for schema-shaped, LLM-assisted structured extraction from one or more URLs.
knowledge-base
Use when crawling docs, ingesting source families, building or refreshing a Qdrant-backed RAG corpus, capturing docs sections, or producing LLM-ready markdown.
knowledge-ingest
Use when indexing docs portals where Axon Chrome rendering, capture-time automation scripts, or host browser-assisted URL discovery are needed.
lead-gen
Use when generating CRM-ready lead lists from public or authorized sources with axon search, research, scrape, map, or extraction.
lead-research
Produce pre-meeting lead intelligence briefs with Axon. Use when the user needs company research, person research, recent news, talking points, pain points, or outreach preparation before a sales call, partnership meeting, investor conversation, or customer interview.
market-research
Use for informational market, industry, company, earnings, or public financial research with Axon, not personalized investment advice.
qa
QA test a live website with Axon discovery/content evidence plus browser automation when interaction is required. Use when the user wants exploratory QA, form testing, navigation/link checks, responsive checks, performance observations, bug reports, or a pre-launch quality review.
rag-synthesize
RAG synthesis prompt for axon ask — source-grounded, depth-adaptive, injection-hardened. Loaded at runtime by src/vector/ops/commands/ask/synthesis_prompt.rs.
research-papers
Use when the user wants a literature review, paper summary, research landscape, or sourced synthesis from papers, reports, abstracts, or converted PDFs.
seo-audit
Use when auditing SEO with axon map, scrape, search, rendered checks, metadata review, sitemap/site analysis, SERP comparison, or scraped-page findings.
using-axon
Use Axon for RAG, web search, source indexing, extraction, memory, and grounded answers over indexed docs, repos, feeds, or web content.
website-design-clone
Use when the user wants an agent-ready DESIGN.md derived from a site's observable visual language using Axon brand, scrape, and screenshot evidence.
workflows
Route outcome-focused Axon requests only when no more specific bundled workflow skill is already selected.
vaultspec-adr
Capture an architectural decision as an ADR in .vault/adr/. Use after research, before planning, when a significant design choice and its trade-offs must be recorded.
vaultspec-code-research
Ground a coding task in real source code, reference implementations, and library docs. Use before implementing a complex feature or when documentation is thin.
vaultspec-code-review
Run a formal code review for safety, intent, and quality. Use to verify completed work before marking it done.
vaultspec-curate
Reconcile the ADR architecture corpus against the codebase and the feature lifecycle documents against the single-home-fact boundary. Use to audit ADR status and supersession, find ADR-vs-ADR, ADR-vs-code, and document-vs-document conflicts (restated grounding, displaced decisions, forked facts), and action them. Mechanical .vault/ hygiene is the CLI's job; this skill does the semantic reconciliation the CLI cannot.
vaultspec-documentation
Write one polished user-facing document through a structured pipeline. Use to create or rewrite a README, guide, or feature doc.
vaultspec-execute
Execute an approved implementation plan, dispatching agent personas per step. Use when a plan document is ready to build.
vaultspec-projectmanager
Coordinate GitHub Projects: triage issues, track milestones, provision worktrees, manage releases. Use for project management outside the pipeline.
vaultspec-rag-discovery
Semantic codebase and architecture-decision discovery with vaultspec-rag - find code and the ADRs that govern it by meaning, then narrow with advanced filters and noise controls. Use to locate where or how something is done, or the decision behind it, instead of guessing identifiers or sweeping with keyword/grep search.
vaultspec-research
Explore an unfamiliar problem and weigh options before committing. Use when unsure how to approach a complex feature, refactor, or bug.
vaultspec-team
Start a multi-agent coding team for a hard challenge. Use when a problem is too large for a single agent.
vaultspec-write
Write an implementation plan of waves, phases, and steps. Use only after the authorizing ADR - or the cluster of ADRs a roll-up plan executes - is approved.
rag-poisoning
Expert methodology for attacking Retrieval-Augmented Generation (RAG) pipelines through document poisoning, index corruption, adversarial queries, and retrieval manipulation. For authorized red team assessments of AI search and Q&A systems.
infra_advisor
Audit the local cluster's hardware/software/MCP setup and recommend changes that cut token cost — GPU upgrades, Hailo NPU for vision, moving the inference node to Linux, running Qdrant locally, wiring MCP — with an ASCII cluster diagram. Also provides an auto one-pass audit (directives + infra + code duplication + skills) on the project. Use when the user asks how to reduce token/usage cost via hardware/infra, wants cluster setup tips, an ASCII diagram of their setup, or a quick all-in-one audit.
ingest
Local-first web scraping and source ingestion — fetch a URL, extract clean text locally (0 cloud tokens), optionally structure it with a local model, and store it in a Qdrant collection (the second-brain "foundation") for later recall. Use for /web-scraping, /ingest-source, building a knowledge foundation, or recalling past ingested sources. Web fetch + Qdrant over stdlib HTTP.
crawl
Use Axon site-scope source indexing to capture many pages from a site or scoped documentation section.
download
Save website or documentation content locally with Axon map, scrape, site-scope source indexing, output-dir, and screenshots.
map
Use Axon map to discover URLs on a site before scraping, crawling, or extracting.
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