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 EmbrasureAI/spark-observability-skills --skill debug-slow-spark-jobgit clone --depth 1 https://github.com/EmbrasureAI/spark-observability-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/embrasureai/spark-observability-skills/debug-slow-spark-job)<a href="https://agentmods.dev/skills/embrasureai/spark-observability-skills/debug-slow-spark-job"><img src="https://agentmods.dev/badge/skills/embrasureai/spark-observability-skills/debug-slow-spark-job/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/embrasureai/spark-observability-skills/debug-slow-spark-job"><img src="https://agentmods.dev/badge/skills/embrasureai/spark-observability-skills/debug-slow-spark-job.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 112 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00073 | $0.01880 |
| Opus 5 | $0.00036 | $0.00940 |
| Sonnet 5 | $0.00015 | $0.00376 |
| Haiku 4.5 | $0.00007 | $0.00188 |
Grade A, and why
debug-slow-spark-job 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug a slow Spark job
Find the root cause of the slowdown or cost growth. Compare against a healthy run whenever one exists, and normalize for input size before calling anything a regression.
Get the evidence
export SPARK_HISTORY_URL="https://history.example.com"
python3 scripts/spark_history_api.py slow --app-id <slow-application-id> > /tmp/spark-slow.json
python3 scripts/spark_history_api.py slow --app-id <healthy-application-id> > /tmp/spark-healthy.json
Run from this skill directory. If SPARK_HISTORY_URL is unset, find the server before asking the user: try http://localhost:18080, a running application's UI on http://localhost:4040, and the history-server or eventLog settings in the local Spark config; ask only when nothing responds. Authentication comes from SPARK_HISTORY_AUTHORIZATION, SPARK_HISTORY_COOKIE, or SPARK_HISTORY_HEADERS_JSON; never ask for credentials in chat and never disable TLS verification (--ca-file for a private CA).
Other subcommands: applications [--status completed|running] to find app IDs, sql-list --app-id <id> and sql --app-id <id> --execution-id <n> for the executed plan with per-node metrics, failure --app-id <id> for failed-stage detail; all accept --stage-limit N --task-limit N. For anything the profiles omit, call $SPARK_HISTORY_URL/api/v1 directly with the same auth headers: /applications/{app}/jobs, /stages/{stage}/{attempt}/taskSummary?quantiles=0.05,0.5,0.95, /stages/{stage}/{attempt}/taskList?sortBy=-runtime, /allexecutors, /environment, /sql/{execution}?details=true&planDescription=true.
In taskSummary, metric arrays align with quantiles [0, 0.5, 0.95, 0.99, 1.0]: the middle entry is the median, the last is the max. Read spark.master and deploy mode from environment.sparkProperties to know where driver and executor logs live. Compare stage timelines between the two runs and start where they first diverge; wall time alone mixes queue time, driver work, execution, and commit.
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.
- 12d ago First seen · 113 lines · 73 tokens per session scan A 4da606a29aa0
debug-slow-spark-job is a skill published in the GitHub repository EmbrasureAI/spark-observability-skills (52 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 1,880 once invoked, about $0.0004 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
notebooklm
Install, authenticate, troubleshoot, and operate Gemini Notebook through the notebooklm-py CLI or typed async Python API. Use for notebook and source management, grounded chat and research, and artifact generation or download when the user mentions Gemini Notebook, notebooklm-py, the notebooklm CLI, or its Python API.…
debug-inference
Debug inference clients that use an attached provider and its native endpoint, including hosted APIs and host-local Ollama, vLLM, SGLang, TRT-LLM, LM Studio, or NIM. Use for provider attachment, endpoint policy, credential substitution, topology, and migration from the removed inference.local endpoint. Trigger…
oh-my-posh
Install, configure, or troubleshoot Oh My Posh/ohmyposh: shell init, themes, segments, Nerd Font icons, and prompt setup on PowerShell, zsh, bash, or fish.
eagle3-triage
Triage a failed EAGLE3 pipeline run. Identifies which step failed (data synthesis, hidden state dump, training, or benchmark), diagnoses root cause from logs, and suggests fixes. Use when user reports an EAGLE3 pipeline failure or asks why a specific step failed. Also helps debug new model support issues.
setup
Configure MLflow tracing for Claude Code.
trulens-instrumentation
Instrument LLM apps with TruLens OTEL-based tracing - from setup to debugging and optimization.