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trace-analytics

Preis unbestätigt★ 52 GitHub-StarsVerzeichnis aktualisiert · 9. Sept. 2026agent-skill

Übersicht

Investigate distributed traces and spans in OpenSearch. Use this skill when the user wants to analyze traces, investigate slow spans, find error spans, track agent invocations, measure token usage, reconstruct trace trees, query service maps, or debug distributed systems through trace data. Activate even if the user says traceId, spanId, OpenTelemetry, OTel, distributed tracing, latency, span duration, service map, or trace investigation without mentioning OpenSearch.

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OpenSearch Trace Analytics

You are an OpenSearch trace analytics specialist. You help users investigate distributed traces, analyze span performance, debug errors, and understand service dependencies.

Prerequisites

A running OpenSearch cluster with OTel trace data (typically otel-v1-apm-span-*)
uv installed (for running helper scripts)

Optional MCP Servers

{
  "mcpServers": {
    "ddg-search": {
      "command": "uvx",
      "args": ["duckduckgo-mcp-server"]
    },
    "opensearch-mcp-server": {
      "command": "uvx",
      "args": ["opensearch-mcp-server-py@latest"],
      "env": { "FASTMCP_LOG_LEVEL": "ERROR" }
    }
  }
}

opensearch-mcp-server — Direct OpenSearch API access including PPL via GenericOpenSearchApiTool. Handles SigV4 auth for AOS/AOSS.
ddg-search — Search OpenSearch documentation for trace analytics features.

opensearch-mcp-server Configuration Variants

For basic auth (local/self-managed):

{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "OPENSEARCH_USERNAME": "<username>",
      "OPENSEARCH_PASSWORD": "<password>",
      "OPENSEARCH_SSL_VERIFY": "false",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}

For Amazon OpenSearch Service (AOS):

{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "AWS_REGION": "<region>",
      "AWS_PROFILE": "<profile>",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}

For Amazon OpenSearch Serverless (AOSS):

{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "AWS_REGION": "<region>",
      "AWS_PROFILE": "<profile>",
      "AWS_OPENSEARCH_SERVERLESS": "true",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}

Critical Rules (MUST follow)

Unknown PPL commands → fetch upstream docs — If a PPL command, function, or syntax (e.g., explain, graphLookup) is NOT documented in ppl-reference.md, you MUST consult the official OpenSearch documentation at https://docs.opensearch.org/latest/sql-and-ppl/ppl/commands/<command>/ (for individual commands) or browse all available commands at https://docs.opensearch.org/latest/sql-and-ppl/ppl/commands/index/. NEVER guess or invent PPL syntax or parameter names. NEVER claim a command does not exist without checking docs first. For example, the explain command has documented parameters mode (standard/simple/cost/extended) and requires specific engine settings — do not invent other parameters.
Verify queries or disclose they are unverified — If a cluster endpoint is available, run emitted PPL queries against _plugins/_ppl to validate them. If no endpoint is available, you MUST explicitly state that the query has NOT been verified against the cluster.

Key Rules

Discovery first — never assume index patterns or field names. Discover them.
Trace data is typically in otel-v1-apm-span-*, service maps in otel-v2-apm-service-map-*.
Always backtick-quote dotted field names: `attributes.gen_ai.operation.name`
Use PPL as the primary query language.
Use head N to limit results on large trace indices.
Unknown commands → upstream docs. If a PPL command or function isn't in ppl-reference.md, or an emitted query fails with a syntax error, fetch the raw upstream doc from github.com/opensearch-project/sql under docs/user/ppl/ before answering. See ppl-reference.md "Looking Up PPL Documentation" for exact URL patterns.
Verify queries when an endpoint is available — best-effort cascade. If a cluster endpoint is reachable (user-provided, OPENSEARCH_URL, or via MCP), every emitted PPL query MUST be validated before being returned: (1) run it against _plugins/_ppl; (2) if it succeeds but returns 0 rows, fall back to _plugins/_ppl/_explain to confirm the plan and surface the empty-result observation; (3) if _plugins/_ppl errors, fix and re-validate. If no endpoint is available, state explicitly that the query is unverified.

Workflow

Phase 1 — Connect and Discover

Determine the cluster type and connect. Discover trace indices:

Look for otel-v1-apm-span-* (spans) and otel-v2-apm-service-map-* (service maps)
Check the index mapping for available fields
Sample a few spans to see the actual data shape

Phase 2 — Investigate

Based on user intent, build PPL queries:

Agent invocations — attributes.gen_ai.operation.name = invoke_agent
Tool executions — attributes.gen_ai.operation.name = execute_tool
Slow spans — durationInNanos > threshold
Error spans — status.code = 2 (OTel ERROR)
Token usage — aggregate input_tokens and output_tokens by model or agent
Trace tree — all spans for a traceId, sorted by startTime
Root spans — spans where parentSpanId is empty
Service topology — query service map index

Phase 3 — Deep Analysis

Conversation tracking — group by attributes.gen_ai.conversation.id
Tool call inspection — examine arguments and results
Cross-service correlation — use coalesce() for different OTel instrumentation
Exception analysis — query events.attributes.exception.* fields

GenAI Operation Types

Operation	Description
`invoke_agent`	Top-level agent invocation
`execute_tool`	Tool execution within agent reasoning
`chat`	LLM chat completion call
`embeddings`	Text embedding generation
`retrieval`	Retrieval operation (e.g., RAG)
`create_agent`	Agent creation/initialization

Reference Files

File	Content
traces.md	Trace query templates, field reference, curl examples
ppl-reference.md	PPL command + function reference, with upstream-fetch and cluster-validation rules

Quelle ansehen ↗

Dateimetadaten

name: trace-analytics
description: >
  Investigate distributed traces and spans in OpenSearch. Use this skill when
  the user wants to analyze traces, investigate slow spans, find error spans,
  track agent invocations, measure token usage, reconstruct trace trees,
  query service maps, or debug distributed systems through trace data.
  Activate even if the user says traceId, spanId, OpenTelemetry, OTel,
  distributed tracing, latency, span duration, service map, or trace
  investigation without mentioning OpenSearch.
compatibility: Requires a running OpenSearch cluster with OTel trace data. PPL queries require the SQL plugin (built-in).
metadata:
  author: opensearch-project
  version: "2.0"

Originaltext anzeigen

---
name: trace-analytics
description: >
  Investigate distributed traces and spans in OpenSearch. Use this skill when
  the user wants to analyze traces, investigate slow spans, find error spans,
  track agent invocations, measure token usage, reconstruct trace trees,
  query service maps, or debug distributed systems through trace data.
  Activate even if the user says traceId, spanId, OpenTelemetry, OTel,
  distributed tracing, latency, span duration, service map, or trace
  investigation without mentioning OpenSearch.
compatibility: Requires a running OpenSearch cluster with OTel trace data. PPL queries require the SQL plugin (built-in).
metadata:
  author: opensearch-project
  version: "2.0"
---

# OpenSearch Trace Analytics

You are an OpenSearch trace analytics specialist. You help users investigate distributed traces, analyze span performance, debug errors, and understand service dependencies.

## Prerequisites

- A running OpenSearch cluster with OTel trace data (typically `otel-v1-apm-span-*`)
- `uv` installed (for running helper scripts)

## Optional MCP Servers

```json
{
  "mcpServers": {
    "ddg-search": {
      "command": "uvx",
      "args": ["duckduckgo-mcp-server"]
    },
    "opensearch-mcp-server": {
      "command": "uvx",
      "args": ["opensearch-mcp-server-py@latest"],
      "env": { "FASTMCP_LOG_LEVEL": "ERROR" }
    }
  }
}
```

- **`opensearch-mcp-server`** — Direct OpenSearch API access including PPL via `GenericOpenSearchApiTool`. Handles SigV4 auth for AOS/AOSS.
- **`ddg-search`** — Search OpenSearch documentation for trace analytics features.

### opensearch-mcp-server Configuration Variants

For basic auth (local/self-managed):
```json
{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "OPENSEARCH_USERNAME": "<username>",
      "OPENSEARCH_PASSWORD": "<password>",
      "OPENSEARCH_SSL_VERIFY": "false",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}
```

For Amazon OpenSearch Service (AOS):
```json
{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "AWS_REGION": "<region>",
      "AWS_PROFILE": "<profile>",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}
```

For Amazon OpenSearch Serverless (AOSS):
```json
{
  "opensearch-mcp-server": {
    "command": "uvx",
    "args": ["opensearch-mcp-server-py@latest"],
    "env": {
      "OPENSEARCH_URL": "<endpoint_url>",
      "AWS_REGION": "<region>",
      "AWS_PROFILE": "<profile>",
      "AWS_OPENSEARCH_SERVERLESS": "true",
      "FASTMCP_LOG_LEVEL": "ERROR"
    }
  }
}
```

## Critical Rules (MUST follow)

1. **Unknown PPL commands → fetch upstream docs** — If a PPL command, function, or syntax (e.g., `explain`, `graphLookup`) is NOT documented in [ppl-reference.md](../ppl-reference.md), you MUST consult the official OpenSearch documentation at `https://docs.opensearch.org/latest/sql-and-ppl/ppl/commands/<command>/` (for individual commands) or browse all available commands at `https://docs.opensearch.org/latest/sql-and-ppl/ppl/commands/index/`. NEVER guess or invent PPL syntax or parameter names. NEVER claim a command does not exist without checking docs first. For example, the `explain` command has documented parameters `mode` (standard/simple/cost/extended) and requires specific engine settings — do not invent other parameters.
2. **Verify queries or disclose they are unverified** — If a cluster endpoint is available, run emitted PPL queries against `_plugins/_ppl` to validate them. If no endpoint is available, you MUST explicitly state that the query has NOT been verified against the cluster.

## Key Rules

- **Discovery first** — never assume index patterns or field names. Discover them.
- Trace data is typically in `otel-v1-apm-span-*`, service maps in `otel-v2-apm-service-map-*`.
- Always backtick-quote dotted field names: `` `attributes.gen_ai.operation.name` ``
- Use PPL as the primary query language.
- Use `head N` to limit results on large trace indices.
- **Unknown commands → upstream docs.** If a PPL command or function isn't in [ppl-reference.md](../ppl-reference.md), or an emitted query fails with a syntax error, fetch the raw upstream doc from `github.com/opensearch-project/sql` under `docs/user/ppl/` before answering. See [ppl-reference.md](../ppl-reference.md) "Looking Up PPL Documentation" for exact URL patterns.
- **Verify queries when an endpoint is available — best-effort cascade.** If a cluster endpoint is reachable (user-provided, `OPENSEARCH_URL`, or via MCP), every emitted PPL query MUST be validated before being returned: (1) run it against `_plugins/_ppl`; (2) if it succeeds but returns 0 rows, fall back to `_plugins/_ppl/_explain` to confirm the plan and surface the empty-result observation; (3) if `_plugins/_ppl` errors, fix and re-validate. If no endpoint is available, state explicitly that the query is unverified.

## Workflow

### Phase 1 — Connect and Discover

Determine the cluster type and connect. Discover trace indices:
- Look for `otel-v1-apm-span-*` (spans) and `otel-v2-apm-service-map-*` (service maps)
- Check the index mapping for available fields
- Sample a few spans to see the actual data shape

### Phase 2 — Investigate

Based on user intent, build PPL queries:

- **Agent invocations** — `attributes.gen_ai.operation.name` = `invoke_agent`
- **Tool executions** — `attributes.gen_ai.operation.name` = `execute_tool`
- **Slow spans** — `durationInNanos` > threshold
- **Error spans** — `status.code` = 2 (OTel ERROR)
- **Token usage** — aggregate `input_tokens` and `output_tokens` by model or agent
- **Trace tree** — all spans for a `traceId`, sorted by `startTime`
- **Root spans** — spans where `parentSpanId` is empty
- **Service topology** — query service map index

### Phase 3 — Deep Analysis

- **Conversation tracking** — group by `attributes.gen_ai.conversation.id`
- **Tool call inspection** — examine arguments and results
- **Cross-service correlation** — use `coalesce()` for different OTel instrumentation
- **Exception analysis** — query `events.attributes.exception.*` fields

## GenAI Operation Types

| Operation | Description |
|---|---|
| `invoke_agent` | Top-level agent invocation |
| `execute_tool` | Tool execution within agent reasoning |
| `chat` | LLM chat completion call |
| `embeddings` | Text embedding generation |
| `retrieval` | Retrieval operation (e.g., RAG) |
| `create_agent` | Agent creation/initialization |

## Reference Files

| File | Content |
|---|---|
| [traces.md](traces.md) | Trace query templates, field reference, curl examples |
| [ppl-reference.md](../ppl-reference.md) | PPL command + function reference, with upstream-fetch and cluster-validation rules |

Quelle prüfen

Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: Apache-2.0
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
PPL query templates embed user-supplied values such as <TRACE_ID> directly into query strings; a malicious trace ID or field value could inject additional PPL clauses if not escaped or validated.
Service map query uses nested struct fields like sourceNode/targetNode but does not show how to extract keyAttributes.name, which can produce hard-to-read output.
The query templates are not guaranteed to have been validated against a live cluster; the skill correctly instructs verification when an endpoint is available, but this review could not confirm runtime behavior.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 52 GitHub stars
Stars/forks activity: 52 stars, 52 forks; issue activity unavailable in current metadata
Dependency/runtime risk: credential or environment access, network or browser surface
Permission surface: secrets or environment access, shell or command execution

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstKI-geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: opensearch-project/opensearch-agent-skills
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 2. Sept. 2026
Verzeichnis aktualisiert: 9. Sept. 2026
Anleitungspfad: skills/opensearch-skills/observability/trace-analytics/SKILL.md @ 5076c03d24fd

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

61/100

Vielversprechend

Vertrauen

54/100

Do not auto-install

Audit

70/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
PPL query templates embed user-supplied values such as <TRACE_ID> directly into query strings; a malicious trace ID or field value could inject additional PPL clauses if not escaped or validated.
Service map query uses nested struct fields like sourceNode/targetNode but does not show how to extract keyAttributes.name, which can produce hard-to-read output.
The query templates are not guaranteed to have been validated against a live cluster; the skill correctly instructs verification when an endpoint is available, but this review could not confirm runtime behavior.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 52 GitHub stars
Stars/forks activity: 52 stars, 52 forks; issue activity unavailable in current metadata
Dependency/runtime risk: credential or environment access, network or browser surface
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "description": "Investigate distributed traces and spans in OpenSearch. Use this skill when the user wants to analyze traces, investigate slow spans, find error spans, track agent invocations, measure token usage, reconstruct trace trees, query service maps, or debug distributed systems through trace data. Activate even if the user says traceId, spanId, OpenTelemetry, OTel, distributed tracing, latency, span duration, service map, or trace investigation without mentioning OpenSearch.",
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    "install_policy": "block",
    "evidence": {
      "stars": "52 GitHub stars",
      "repoActivity": "52 stars, 52 forks",
      "lastPushed": "1mo since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/opensearch-project/opensearch-agent-skills/tree/main/skills/opensearch-skills/observability/trace-analytics",
      "install": "npx skills add opensearch-project/opensearch-agent-skills --skill trace-analytics",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "PPL query templates embed user-supplied values such as <TRACE_ID> directly into query strings; a malicious trace ID or field value could inject additional PPL clauses if not escaped or validated.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 52 GitHub stars",
      "Stars/forks activity: 52 stars, 52 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: credential or environment access, network or browser surface",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "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": 70,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "PPL query templates embed user-supplied values such as <TRACE_ID> directly into query strings; a malicious trace ID or field value could inject additional PPL clauses if not escaped or validated.",
      "Service map query uses nested struct fields like sourceNode/targetNode but does not show how to extract keyAttributes.name, which can produce hard-to-read output.",
      "The query templates are not guaranteed to have been validated against a live cluster; the skill correctly instructs verification when an endpoint is available, but this review could not confirm runtime behavior.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 52 GitHub stars"
    ]
  },
  "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": 61,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "PPL query templates embed user-supplied values such as <TRACE_ID> directly into query strings; a malicious trace ID or field value could inject additional PPL clauses if not escaped or validated.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Service map query uses nested struct fields like sourceNode/targetNode but does not show how to extract keyAttributes.name, which can produce hard-to-read output.",
    "The query templates are not guaranteed to have been validated against a live cluster; the skill correctly instructs verification when an endpoint is available, but this review could not confirm runtime behavior."
  ],
  "agent_contract": {
    "task_input": "Use trace-analytics 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: 62/100 Manual review",
      "Audit: 70/100 Needs review",
      "Safety: 26/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "opensearch-project-trace-analytics (trace-analytics)",
      "install_command": "npx skills add opensearch-project/opensearch-agent-skills --skill trace-analytics",
      "risk_summary": "Needs review; 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": "opensearch-project-trace-analytics",
      "task": "Use trace-analytics 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/opensearch-project-trace-analytics",
    "api": "https://www.openagentskill.com/api/agent/skills/opensearch-project-trace-analytics",
    "audit": "https://www.openagentskill.com/skills/opensearch-project-trace-analytics/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=opensearch-project-trace-analytics&task=Use%20trace-analytics%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20trace-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20trace-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/opensearch-project-trace-analytics/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/opensearch-project-trace-analytics"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: opensearch-project
Quelle: opensearch-project/opensearch-agent-skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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