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thousandeyes-network-data-from-traceid

Obtain ThousandEyes Network & App Synthetics data given a trace ID. Use when a user has a `traceId`, ThousandEyes MCP is available, and one or more Observability Platform integrations or equivalent tooling paths are available to query every relevant Observability Platform for the trace, extract `tracestate` or `w3c.tracestate`, decode the embedded ThousandEyes permalink, recover the ThousandEyes account/test/agent/execution identifiers, and fetch the matching ThousandEyes network data.

95

1.07x
Quality

92%

Does it follow best practices?

Impact

99%

1.07x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a strong, well-structured skill for a complex multi-system correlation workflow. Its greatest strengths are the highly actionable guidance with specific tool names, attribute paths, and a worked parsing example, plus a clear 5-phase workflow with explicit validation checkpoints and stop conditions. The main weakness is moderate redundancy between the 'Required Behavior' section and the 'Workflow' section, which could be consolidated to save tokens.

DimensionReasoningScore

Conciseness

The skill is reasonably efficient for a complex multi-step workflow, but includes some redundancy (e.g., the tool discovery instruction is repeated in both 'Required Behavior' and 'Workflow' step 1, and some guardrails restate what's already in the workflow). The inline parsing example earns its place, but overall it could be tightened by ~20%.

2 / 3

Actionability

The skill provides concrete, specific steps with exact attribute names (tracestate, w3c.tracestate, te=), exact parameter mappings (__a→accountId, testId, agentId, startTime→executionTime), specific tool names (get_network_app_synthetics_test, get_path_visualization_results, etc.), and a fully worked inline parsing example with real-looking values. This is highly actionable and copy-paste ready.

3 / 3

Workflow Clarity

The workflow is clearly sequenced across 5 numbered phases with explicit sub-steps. It includes validation checkpoints (comparing identifiers across platforms, verifying tracestate presence before proceeding), clear stop conditions in guardrails (stop if tracestate absent, stop if identifiers missing), and fallback paths (span-search, log-search). The feedback loop for disagreements between platforms is explicitly called out.

3 / 3

Progressive Disclosure

The skill provides a clear overview with well-signaled one-level-deep references to reference.md (for parsing rules and query strategy) and examples.md (for structured reports). The inline content is appropriately scoped to the essential workflow, with detailed parsing rules and report templates deferred to supporting files. References are contextually placed where they're needed.

3 / 3

Total

11

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong, highly specific skill description that clearly defines a narrow technical workflow involving ThousandEyes trace correlation. It includes an explicit 'Use when' clause with precise preconditions and lists concrete actions in the pipeline. The only minor concern is that the description is quite long and dense, which could slightly reduce readability, but it does not sacrifice clarity for brevity.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions: obtain ThousandEyes data given a trace ID, query Observability Platforms for the trace, extract tracestate/w3c.tracestate, decode embedded ThousandEyes permalink, recover account/test/agent/execution identifiers, and fetch matching network data.

3 / 3

Completeness

Clearly answers both 'what' (obtain ThousandEyes Network & App Synthetics data, decode permalinks, fetch network data) and 'when' (explicit 'Use when' clause specifying a user has a traceId, ThousandEyes MCP is available, and Observability Platform integrations are available).

3 / 3

Trigger Term Quality

Includes highly specific natural keywords a user would mention: 'traceId', 'ThousandEyes', 'tracestate', 'w3c.tracestate', 'permalink', 'Network & App Synthetics', 'Observability Platform', and 'MCP'. These are the exact terms someone working in this domain would use.

3 / 3

Distinctiveness Conflict Risk

Extremely niche and specific to ThousandEyes + trace ID correlation workflows. The combination of ThousandEyes MCP, tracestate decoding, and Observability Platform querying makes it highly unlikely to conflict with any other skill.

3 / 3

Total

12

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
thousandeyes/thousandeyes-ai-agents-toolkit
Reviewed

Table of Contents

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