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-spark-failuregit 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-spark-failure)<a href="https://agentmods.dev/skills/embrasureai/spark-observability-skills/debug-spark-failure"><img src="https://agentmods.dev/badge/skills/embrasureai/spark-observability-skills/debug-spark-failure/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-spark-failure"><img src="https://agentmods.dev/badge/skills/embrasureai/spark-observability-skills/debug-spark-failure.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 80 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 | $0.00059 | $0.01613 |
| Opus 5 | $0.00030 | $0.00807 |
| Sonnet 5 | $0.00012 | $0.00323 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
debug-spark-failure 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 10d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug a Spark failure
Find the root cause: the earliest failure that explains the rest of the chain. Later fetch failures, retries, and executor loss are usually fallout, not cause.
Get the evidence
export SPARK_HISTORY_URL="https://history.example.com"
python3 scripts/spark_history_api.py applications --status completed --limit 20
python3 scripts/spark_history_api.py failure --app-id <application-id> > /tmp/spark-failure.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: slow --app-id <id> for longest-stage distributions, sql-list / sql --app-id <id> --execution-id <n> for the executed plan; 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?status=failed, /allexecutors, /environment.
The History Server has no driver logs or container termination reasons: read spark.master and spark.submit.deployMode from environment.sparkProperties to locate them, then pull the driver log around the first exception, the first failing executor's log (kubectl logs --previous, yarn logs -applicationId, or the platform's log store), and the cluster manager's reason for any lost container. A driver crash can leave no failed Spark job at all. In taskSummary, metric arrays align with quantiles: the middle entry is the median, the last is the max.
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.
- 10d ago First seen · 81 lines · 59 tokens per session scan A 301934ba7c50
debug-spark-failure is a skill published in the GitHub repository EmbrasureAI/spark-observability-skills (52 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,613 once invoked, about $0.0003 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.
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