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 Consensys/ask-o11y-plugin --skill analyzing-tracesgit clone --depth 1 https://github.com/Consensys/ask-o11y-pluginWrote 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/consensys/ask-o11y-plugin/analyzing-traces)<a href="https://agentmods.dev/skills/consensys/ask-o11y-plugin/analyzing-traces"><img src="https://agentmods.dev/badge/skills/consensys/ask-o11y-plugin/analyzing-traces/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/consensys/ask-o11y-plugin/analyzing-traces"><img src="https://agentmods.dev/badge/skills/consensys/ask-o11y-plugin/analyzing-traces.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 | $0.00080 | $0.00637 |
| Opus 5.5 | $0.00032 | $0.00255 |
| Sonnet 5.5 | $0.00016 | $0.00127 |
| Haiku 4.5 | $0.00008 | $0.00064 |
Grade A, and why
analyzing-traces 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 1mo 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trace analysis workflow
- Discover the schema first — List available trace attribute names and values with the trace attribute tools before constructing TraceQL queries; never guess attribute names.
- Scope by service — Anchor queries on
resource.service.namefor the affected services; add narrow attribute filters from there. - Pick the slice — Error traces (
status=error), slow traces (duration > 1s), or a specific route/operation — one hypothesis per query. - Bound the search — Always pass explicit time bounds on search to limit scope; add
with(most_recent=true)when you need deterministic recent results.
TraceQL quick reference
Attribute scopes and intrinsics:
resource.*for resource attributes (e.g.,resource.service.name,resource.deployment.environment)span.*for span attributes (e.g.,span.http.method,span.db.statement)event.*for events- Intrinsics:
name,status(ok/error/unset),duration,kind(server/client/producer/consumer/internal),traceDuration,rootServiceName,rootName
Structural operators (root-cause patterns):
{ kind = server } >> { status = error }— server span with a downstream error (descendant){ span.db.system = "redis" } && { span.db.system = "postgresql" }— both present anywhere in the trace{ !resource.service.name="healthcheck" }— NOT
Pipelines and metrics:
{ status = error } | count() >= 2— traces with at least two error spans{ status = error } | rate() by (resource.service.name)— error rate per service (metrics from traces){ kind = server } | quantile_over_time(duration, .99) by (resource.service.name)— p99 latency{ status = error } | select(span.http.url, duration)— project specific fields
See references/traceql.md for the full cheat sheet — scopes, operators, structural queries, and metric functions.
Interpreting results
For each matching trace, report the slowest or failing spans and their downstream calls; correlate timing with metrics spikes and log lines for the same window before concluding. Structural queries (>>, &&) are the fastest way to separate "this service failed" from "this service observed a downstream failure".
What ships with it
1 file 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.
- 1mo ago Changed · +14 lines · +33 tokens per session d7e6fe5728fd
- 1mo ago First seen · 31 lines · 47 tokens per session scan A c38c0fed87c8
analyzing-traces is a skill published in the GitHub repository Consensys/ask-o11y-plugin (42 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 637 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-07.
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