Qdrant Advisor
QdrantOfficialPublished by Qdrant
- Skill
- Tool integration
- Software development
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The qdrant-advisor skill provides a structured, iterative methodology for diagnosing and resolving issues in Qdrant vector search deployments. Rather than relying on static internal knowledge, it instructs the agent to dynamically fetch the most current, authoritative troubleshooting steps from the official Qdrant skill registry. By following a hierarchical, depth-first traversal of diagnostic branches, the agent can pinpoint issues ranging from performance bottlenecks and memory pressure to scaling and configuration errors, ensuring that advice is always grounded in the latest documentation and best practices.
What it does
- Frames user symptoms into targeted search queries for the Qdrant skill registry.
- Navigates hierarchical skill trees to find specific diagnostic procedures.
- Follows lateral links between related skills to address multi-faceted problems.
- Synthesizes actionable, ordered diagnostic steps based on live-fetched context.
- Surfaces critical 'what NOT to do' warnings to prevent common configuration errors.
- Cites official documentation URLs for further user verification.
When to use it
- When a user reports performance issues like slow search or indexing latency.
- When troubleshooting resource constraints such as high memory usage or OOM crashes.
- When planning scaling, sharding, or infrastructure migration decisions.
- When resolving configuration or compatibility issues during version upgrades.
- When debugging unexpected search results or hybrid search behavior.
Advantages
- Ensures advice is always current by avoiding reliance on potentially stale training data.
- Provides a consistent, repeatable diagnostic process for complex distributed systems.
- Reduces human error by surfacing explicit warnings and 'what NOT to do' guidance.
- Connects users directly to canonical documentation for deeper investigation.
Usage examples
- “My Qdrant node is crashing with OOM errors, what should I check?”
- The agent will search the Qdrant registry for memory-related diagnostic skills, traverse the relevant hierarchy to identify common OOM causes, and provide a prioritized list of metrics and configuration checks.
- “We are seeing slow search latency after a bulk upload, how do I optimize?”
- The agent will fetch performance-optimization skills, guide the user through checking optimizer status and indexing health, and suggest appropriate scaling or configuration adjustments.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/qdrant/qdrant-advisor/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip qdrant-advisor.zip -d .agents/skills
Documentation by Qdrant
Qdrant Troubleshooting & Advisory
Core principle
Do not answer Qdrant questions from memory. Qdrant evolves quickly (new endpoints, metrics, defaults, and deployment patterns land often), and the authoritative, current guidance lives at skills.qdrant.tech as a hierarchy of agent skills. Your job is to load the relevant skill context live, then ground your diagnosis in it — loading only the branch that matches the problem, never the whole tree.
You are consuming these skills as context. You are not installing them and nothing needs to be installed.
The knowledge source
Search:
https://skills.qdrant.tech/search?query=your+query+hereThe structure is hierarchical: top-level skill
SKILL.md→ sub-skillSKILL.md→ linked documentation pages. Each level narrows scope. Traverse it depth-first, following only the branch(es) that match the symptom.
Workflow
1. Frame the problem
Pull out the concrete details before fetching anything:
The symptom(s) in the user's words (e.g. "memory keeps climbing", "queries got slow after a bulk upload", "results are irrelevant").
The deployment type (local, Docker, self-hosted, Cloud, embedded) and version, if known.
What changed recently (upgrade, new index, traffic spike, model swap).
Turn these into 1–3 short search phrases.
2. Find the right skill(s)
Use Search (fastest path to the right skill). Fetch https://skills.qdrant.tech/search?query=<your query>, substituting your phrase for your+query+here (encode spaces as + or %20). It returns the single most relevant top-level skill's SKILL.md. Run it more than once for multi-part problems (e.g. one search for the memory symptom, one for the scaling question).
3. Traverse the hierarchy (deep and lateral)
Each SKILL.md you load names its sub-skills (and often related skills and docs) as links. The hierarchy is not just two levels — a skill can nest several layers deep, and skills also reference each other laterally. Follow the links, not a fixed depth.
Descend (go deeper). A SKILL.md is not necessarily a leaf just because you fetched it. If its sections themselves point to further SKILL.md files, keep descending along the branch that matches the symptom — top-level → sub-skill → sub-sub-skill → … — until you reach a level whose guidance is concrete enough to act on (ordered diagnostic steps, exact endpoints/metrics, an explicit "what NOT to do" list). Don't stop early at an intermediate skill that only routes you onward.
Move laterally (go sideways). Real problems often span areas. Follow a link to a sibling or related skill when:
the current skill explicitly points to another (e.g. a debugging skill that says "if this is actually a capacity problem, see scaling"),
the symptom has more than one plausible cause living under different top-level skills (e.g. slow queries could be a monitoring/optimizer issue or a performance-optimization issue or a scaling issue), or
you ran multiple searches in step 2 and they surfaced different skills, each covering part of the problem.
Load each relevant branch, then reconcile what they say in step 4.
Stay disciplined about relevance. Going deep and going sideways is encouraged when the problem warrants it — but still load only branches that bear on the symptom. Don't sweep in unrelated siblings, and stop expanding once you can give a complete, grounded answer. The goal is "all the relevant context and nothing else," not "the whole tree."
Documentation pages. Skills link out to canonical docs (e.g. …/md/documentation/…, qdrant.tech/documentation/…, or qdrant.tech/articles/…). Fetch these links exactly as the SKILL.md provides them — they render as clean markdown natively. Pull a doc page only when you need detail a SKILL.md references but does not itself contain.
4. Diagnose and advise
Synthesize an answer strictly from the loaded context:
State the most likely cause(s) in priority order — the skills often tell you what to check first (e.g. "check optimizer status before blaming search latency"); preserve that ordering.
Give concrete, ordered steps: the endpoints to hit, the metrics to read and their thresholds, the config to change.
Surface the skill's "what NOT to do" warnings explicitly — they prevent common self-inflicted damage.
Cite the canonical Qdrant doc URLs you relied on so the user can go deeper.
If the loaded context does not cover the case, say so plainly and either run a different search or fall back to the catalog — do not paper over the gap with remembered guesses.
Operating notes
Always fetch fresh every session. Never reuse a previously cached copy of a skill; the registry updates and staleness is exactly what this approach avoids.
Do not install anything. You are loading context only.
Fetching: every URL you need is either in this skill (root index, search base) or surfaced by a page you already fetched (links inside a
SKILL.mdor the root index), so each is fetchable as-is. If a constructed search-query URL is ever rejected, fall back to fetching the root index and navigate from its absolute links.
Example Workflow
Symptom: "Our Qdrant node's RAM keeps climbing and it OOM-killed last night. Nothing obvious changed."
Search: skills.qdrant.tech/search?query=qdrant+memory+growing+OOM
Follow any sub-skill link on memory or debugging that the returned page names.
Hop laterally to the scaling skill it references, if capacity is a plausible alternative cause.
Synthesize from what you loaded; cite the doc URLs. If nothing loaded covers the case, say so; don't fill from memory.
Files
- ATTRIBUTION.mdAdded by AutoGineer838 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.7 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Node.js
Security
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Attribution and licence
- Published by
- Qdrant
- Publisher
- Qdrant (Official)
- Source
- qdrant/skills · meta/qdrant-advisor · commit 9f55f6a
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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Download ZIP
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