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 HorizonRobotics/OE-Skills --skill j6-ucp-perfetto-trace-analysisgit clone --depth 1 https://github.com/HorizonRobotics/OE-SkillsWrote 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/horizonrobotics/oe-skills/j6-ucp-perfetto-trace-analysis)<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/j6-ucp-perfetto-trace-analysis"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/j6-ucp-perfetto-trace-analysis/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/horizonrobotics/oe-skills/j6-ucp-perfetto-trace-analysis"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/j6-ucp-perfetto-trace-analysis.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 | $0.00133 | $0.01642 |
| Opus 5 | $0.00067 | $0.00821 |
| Sonnet 5 | $0.00027 | $0.00328 |
| Haiku 4.5 | $0.00013 | $0.00164 |
Grade A, and why
j6-ucp-perfetto-trace-analysis 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 12d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UCP Perfetto Inference Analysis
Version: v1.1.0
Use this skill for UCP model inference Perfetto traces when the goal is to quickly find, triage, and explain likely performance issues in the inference path.
It is intentionally scoped: the skill is optimized for fast bottleneck localization inside UCP inference traces, then maps that work onto a fixed set of validated analysis directions so the output stays repeatable instead of drifting into generic Perfetto exploration.
What this skill is for
Use this skill when the user wants to:
- analyze a UCP inference
.pftrace/ Perfetto trace - quickly find likely bottlenecks, hotspots, or latency contributors in the UCP inference path
- investigate slow inference, critical-path delay, CPU/BPU handoff gaps, OpInfer delay, dispatch / response delay, or low effective occupancy
- run one of the validated UCP bottleneck directions in this skill
- generate the default four-direction Markdown report
Do not use this skill for:
- generic Perfetto profiling with no UCP inference focus
- arbitrary performance investigations outside UCP inference
- UI-only trace browsing with no SQL, filtering, or scripted analysis
- non-UCP CPU profiling or unrelated workloads
Supported directions
These four directions are the entire scope of the skill’s default report workflow:
- UCP Critical Path Slice Analysis (Excluding BPU and CPU Operators)
- UCP CPU Operator Slice Analysis
- BPU flow dispatch / response delay
- Corrected BPU effective occupancy
Internal CLI values used by the bundled script:
all(default report mode)dnn_nameopinfer_desc_flowbpu_flow_delaybpu_effective_occupancy
Read references/directions.md for the exact rule definitions, thresholds, grouping semantics, and output expectations.
Default workflow
The bundled analyzer is scripts/analyze_trace.py. Prefer it before writing new automation.
Default behavior:
- Probe the trace schema and relevant argument structure.
- Run all four validated directions.
- Export one Markdown report.
- Return the report path and a short completion note.
- Stop unless the user explicitly asks for deeper follow-up analysis.
What ships with it
9 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.
- 12d ago First seen · 164 lines · 133 tokens per session scan A 0ffc14bec087
j6-ucp-perfetto-trace-analysis is a skill published in the GitHub repository HorizonRobotics/OE-Skills (19 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 133 tokens to every session and 1,642 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.