Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add thousandeyes/thousandeyes-ai-agents-toolkit --skill thousandeyes-network-data-from-traceidgit clone --depth 1 https://github.com/thousandeyes/thousandeyes-ai-agents-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thousandeyes/thousandeyes-ai-agents-toolkit/thousandeyes-network-data-from-traceid)<a href="https://agentmods.dev/skills/thousandeyes/thousandeyes-ai-agents-toolkit/thousandeyes-network-data-from-traceid"><img src="https://agentmods.dev/badge/skills/thousandeyes/thousandeyes-ai-agents-toolkit/thousandeyes-network-data-from-traceid/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/thousandeyes/thousandeyes-ai-agents-toolkit/thousandeyes-network-data-from-traceid"><img src="https://agentmods.dev/badge/skills/thousandeyes/thousandeyes-ai-agents-toolkit/thousandeyes-network-data-from-traceid.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00110 | $0.01557 |
| Opus 5 | $0.00055 | $0.00779 |
| Sonnet 5 | $0.00022 | $0.00311 |
| Haiku 4.5 | $0.00011 | $0.00156 |
Grade A, and why
thousandeyes-network-data-from-traceid scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Obtain ThousandEyes Network Data from TraceID
Use this skill to pivot from an existing distributed trace into the matching ThousandEyes test result. Treat ThousandEyes as the system of record for the recovered test data, and use Observability Platforms to discover the ThousandEyes context from the trace.
Required Behavior
- Use the client's built-in tool discovery for available ThousandEyes and Observability Platform tools. Inspect schemas or argument details before calling unfamiliar tools when that information is available.
- Verify that ThousandEyes MCP is available and that at least one Observability Platform integration or equivalent tooling path is available before starting.
- Build an inventory of every Observability Platform integration or equivalent tooling path available in the current session.
- Query every available Observability Platform by exact
traceId. Do not stop after the first hit. - For every matching trace, inspect trace-level, resource-level, and span-level attributes for
tracestateandw3c.tracestate. - If an Observability Platform lacks direct trace lookup but can search spans or logs by exact
traceId, use that fallback and record it as fallback correlation. - Parse the
tracestatevalue as a W3C vendor-state list and extract thete=member. - URL-decode the ThousandEyes value before reading query parameters.
- Recover
accountIdfrom__a,testIdfromtestId,agentIdfromagentId, andexecutionTimefromstartTime. TreatexecutionTimeas the round selector for the exact ThousandEyes test execution. - Use the recovered identifiers to query ThousandEyes test details and the exact result window, preferring the same agent and the closest execution time.
- If ThousandEyes tools expose time-based round selection instead of a literal
roundId, usestartTimeto select the matching round or result. Do not invent aroundId. - Return both the observability evidence chain and the recovered ThousandEyes data.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 100 lines · 110 tokens per session scan A 8de145e18410
thousandeyes-network-data-from-traceid is a skill published in the GitHub repository thousandeyes/thousandeyes-ai-agents-toolkit (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 110 tokens to every session and 1,557 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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