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 agentmods add skills/rebell-leader/spatialai_mcp/nextflow-debuggingnpx skills add Rebell-Leader/SpatialAI_MCP --skill nextflow-debugginggit clone --depth 1 https://github.com/Rebell-Leader/SpatialAI_MCPWrote 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/rebell-leader/spatialai_mcp/nextflow-debugging)<a href="https://agentmods.dev/skills/rebell-leader/spatialai_mcp/nextflow-debugging"><img src="https://agentmods.dev/badge/skills/rebell-leader/spatialai_mcp/nextflow-debugging.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00520 |
| Opus 5 | $0.00026 | $0.00260 |
| Sonnet 5 | $0.00011 | $0.00104 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
nextflow-debugging 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 6d 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.
What it actually says
Nextflow debugging
The server does not yet parse logs for you (analyze_nextflow_log is roadmap), so
triage the logs directly. Be systematic.
Steps
-
Find the failing task. From the run output, note the process name and the work directory hash (
work/ab/cdef...). The per-task files there are the ground truth:.command.sh— the exact command run..command.err/.command.out— stderr/stdout..command.log,.exitcode— exit status.
-
Classify the error by exit code / message:
- 137 / OOM-killed → memory limit. Increase the process
memorydirective or add a dynamic retry (memory { 8.GB * task.attempt }). - 127 / "command not found" → tool missing in the container; fix the component's engine/setup or the image.
- 126 / permission denied → executable bit / entrypoint issue.
- File/path errors → check channel wiring and input staging; confirm the
input file actually exists and matches the expected format
(run
validate_spatial_data). - Config/DSL errors → confirm DSL2 syntax; OpenProblems pipelines are DSL2.
- 137 / OOM-killed → memory limit. Increase the process
-
Add resilience where appropriate:
errorStrategy 'retry',maxRetries, and dynamic resources bytask.attempt. Don't paper over a real bug with retries. -
Reproduce in isolation. Re-run a single component with
viash runon the test data before re-running the whole pipeline. Use-resumeto avoid recomputing successful tasks. -
Validate inputs with
validate_spatial_data/analyze_spatial_metadatawhen the failure looks data-shaped (wrong format, missing elements, raw-vs-normalized mismatch).
Output
Report: the failing process, the root-cause class, the evidence (the log line), and the specific fix — plus whether it's a data problem, an environment problem, or a code problem.
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.
- 6d ago First seen · 48 lines · 53 tokens per session scan A e091c391a919
nextflow-debugging is a skill published in the GitHub repository Rebell-Leader/SpatialAI_MCP (4 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 520 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 2026-08-31.
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.
azsdk-common-pipeline-analysis
Analyze Azure SDK CI/CD pipeline failures into a structured diagnosis, and define the required output format. Load this skill before calling azsdkanalyzepipeline, which returns raw failure data that this skill interprets and formats. USE FOR: "pipeline failed", "build failure", "CI check failing", "tests failing in…
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…