Activate when the user asks about any repository listed in the system prompt under 'OpenViking — Indexed Code Repositories', or when they ask about an external library, framework, or project that may have been indexed. Also activate when the user wants to add, remove, or manage repos. Always search the local codebase first before using this skill.
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See how to installReview the instructions the agent will receive before you install.
IMPORTANT: All ov commands are terminal (shell) commands — run them via the bash tool. Execute directly — no pre-checks, no test commands. Handle errors when they occur.
OpenViking stores content in a virtual filesystem under the viking:// namespace. Each URI maps to a file or directory, e.g. viking://resources/fastapi/routing.py. Each directory has AI-generated summaries (abstract / overview). The key principle: narrow the URI scope to improve retrieval efficiency. Instead of searching all repos, lock to a specific repo or subdirectory — this reduces noise and speeds up results significantly.
Choose the right command based on what you're looking for:
| Command | Use when | Example |
|---|---|---|
ov search | Semantic search — use for concept/intent based queries | "dependency injection", "how auth works" |
ov grep | You know the exact keyword or symbol | function name, class name, error string |
ov glob | You want to enumerate files by pattern | all *.py files, all test files |
# Semantic search
ov search "dependency injection" --uri viking://resources/fastapi --limit 10
ov search "how tokens are refreshed" --uri viking://resources/fastapi/fastapi/security
ov search "JWT authentication" --limit 10 # across all repos
ov search "error handling" --limit 5 --threshold 0.7 # filter low-relevance results
# Keyword search — exact match or regex
ov grep "verify_token" --uri viking://resources/fastapi
ov grep "class.*Session" --uri viking://resources/requests/requests
# File enumeration — by name pattern (always specify --uri to scope the search)
ov glob "**/*.py" --uri viking://resources/fastapi
ov glob "**/test_*.py" --uri viking://resources/fastapi/tests
ov glob "**/*.py" --uri viking://resources/ # across all repos
Narrowing scope: once you identify a relevant directory, pass it as --uri to restrict subsequent searches to that subtree — this is faster and more precise than searching the whole repo.
Query formulation: write specific, contextual queries rather than single keywords.
ov search "API" # too vague
ov search "REST API authentication with JWT tokens" # better
ov search "JWT token refresh flow" --uri viking://resources/backend # best
# Directories: AI-generated summaries
ov abstract viking://resources/fastapi/fastapi/dependencies/ # one-line summary
ov overview viking://resources/fastapi/fastapi/dependencies/ # detailed breakdown
# Files: raw content
ov read viking://resources/fastapi/fastapi/dependencies/utils.py
ov read viking://resources/fastapi/fastapi/dependencies/utils.py --offset 100 --limit 50
abstract / overview only work on directories. read only works on files.
ov ls viking://resources/ # list all indexed repos
ov ls viking://resources/fastapi # list repo top-level contents
ov ls viking://resources/fastapi --simple # paths only, no metadata
ov ls viking://resources/fastapi --recursive # list all files recursively
ov tree viking://resources/fastapi # full directory tree (default: 3 levels deep)
ov tree viking://resources/fastapi -L 2 # limit depth to 2 levels
ov tree viking://resources/fastapi -l 200 # truncate abstract column to 200 chars
ov tree viking://resources/fastapi -L 2 -l 200 # combined: 2 levels deep, 200-char summaries
-L controls how many levels deep the tree expands. -l controls the length of the AI-generated summary per directory. Use ov tree -L 2 -l 200 as a good starting point to understand a repo's structure before diving in.
ov add-resource https://github.com/owner/repo --to viking://resources/repo --timeout 300
--timeout is required (seconds). Use 300 (5 min) for small repos, increase for larger ones.
After submitting, run ov observer queue once and report status to user. Indexing runs in background — do not poll or wait.
| Repo Size | Files | Est. Time |
|---|---|---|
| Small | < 100 | 2–5 min |
| Medium | 100–500 | 5–20 min |
| Large | 500+ | 20–60+ min |
ov rm viking://resources/fastapi --recursive
This permanently deletes the repo and all its indexed content. Confirm with the user before running.
command not found: ov → Tell user: pip install openviking --upgrade. Stop.
url is required / CLI_CONFIG error → Auto-create config and retry:
mkdir -p ~/.openviking && echo '{"url": "http://localhost:1933"}' > ~/.openviking/ovcli.conf
CONNECTION_ERROR / failed to connect:
~/.openviking/ov.conf exists → auto-start server, wait until healthy, retry:
openviking-server > /tmp/openviking.log 2>&1 &
for i in $(seq 1 10); do ov health 2>/dev/null && break; sleep 3; done
~/.openviking/ov.conf first. Stop.For other issues or command details, run:
ov help
ov <command> --help # e.g. ov search --help
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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