Indexado en Registry

qdrant-advisor

Precio sin confirmar★ 253 Estrellas de GitHubRegistro actualizado · 29 sept 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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+here
The structure is hierarchical: top-level skill SKILL.md → sub-skill SKILL.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.md or 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.

Ver fuente ↗

Metadatos del archivo

name: qdrant-advisor
description: "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."

Ver texto original

---
name: qdrant-advisor
description: "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 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+here`
- The structure is **hierarchical**: top-level skill `SKILL.md` → sub-skill `SKILL.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.md` or 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

1. Symptom: "Our Qdrant node's RAM keeps climbing and it OOM-killed last night. Nothing obvious changed."
2. Search: skills.qdrant.tech/search?query=qdrant+memory+growing+OOM
3. Follow any sub-skill link on memory or debugging that the returned page names.
4. Hop laterally to the scaling skill it references, if capacity is a plausible alternative cause.
5. Synthesize from what you loaded; cite the doc URLs. If nothing loaded covers the case, say so; don't fill from memory.

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: Apache-2.0
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: Apache-2.0

Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Falta aprobación de revisión por IA
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Stars/forks activity: 253 stars, 30 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

IndexadoRevisión estática

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: qdrant/skills
Licencia: Apache-2.0
Versión: Unknown
Último push de GitHub: 29 sept 2026
Registro actualizado: 29 sept 2026
Ruta de instrucciones: meta/qdrant-advisor/SKILL.md @ a4cf493d33e0

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

66/100

Prometedor

Confianza

68/100

Solo sandbox

Auditoría

78/100

Riesgoso

Abrir auditoría completa Ver informe de evaluación

Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Falta aprobación de revisión por IA
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Stars/forks activity: 253 stars, 30 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

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Más detalles

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    "description": "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.",
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      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Stars/forks activity: 253 stars, 30 forks; issue activity unavailable in current metadata",
      "Review status: AI review approval is missing"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 78,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "AI review approval is missing",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Stars/forks activity: 253 stars, 30 forks; issue activity unavailable in current metadata",
      "Review status: AI review approval is missing"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "12d since push",
    "risk": "Risky"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "Audit risk risky exceeds max_risk=medium",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
    "AI review approval is missing",
    "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use qdrant-advisor in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 76/100 Strong shortlist",
      "Audit: 78/100 Risky",
      "Safety: 58/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "qdrant-qdrant-advisor (qdrant-advisor)",
      "install_command": "npx skills add qdrant/skills --skill qdrant-advisor",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "qdrant-qdrant-advisor",
      "task": "Use qdrant-advisor in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/qdrant-qdrant-advisor",
    "api": "https://www.openagentskill.com/api/agent/skills/qdrant-qdrant-advisor",
    "audit": "https://www.openagentskill.com/skills/qdrant-qdrant-advisor/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=qdrant-qdrant-advisor&task=Use%20qdrant-advisor%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20qdrant-advisor%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20qdrant-advisor%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/qdrant-qdrant-advisor/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/qdrant-qdrant-advisor"
  }
}

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Creador: qdrant
Fuente: qdrant/skills
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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