nextflow-debugging

nextflow-debugging is a skill for Claude Code from Rebell-Leader/SpatialAI_MCP. It costs 53 tokens per session (520 once invoked), scanned A, original, Apache-2.0.

A troubleshooting guide for failed Nextflow pipelines in an OpenProblems benchmark. Nextflow is a tool that runs multi-step data workflows, while a benchmark is a repeatable test used for comparison.

In plain words
What is it for?
Inspecting task commands and logs, identifying the cause of a failed process, and choosing appropriate resource or retry changes.
Why use it?
It shows where to find the exact failed task and how common exit codes point to memory, missing tools, permissions, input files, or configuration problems.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: installed under .agents/ (shared by several agents).

Part of the openproblems-spatial plugin — 4 skills, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add skills/rebell-leader/spatialai_mcp/nextflow-debugging
Any agent
npx skills add Rebell-Leader/SpatialAI_MCP --skill nextflow-debugging
Clone the repo
git clone --depth 1 https://github.com/Rebell-Leader/SpatialAI_MCP

Made for: Claude Code.

Or install openproblems-spatial, the plugin that ships this one along with the rest of its 4 skills, 1 MCP server.

Wrote 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.

agentmods badge for nextflow-debugging

README.md
[![agentmods](https://agentmods.dev/badge/skills/rebell-leader/spatialai_mcp/nextflow-debugging.svg)](https://agentmods.dev/skills/rebell-leader/spatialai_mcp/nextflow-debugging)
Your own site
<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>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 520 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 6d ago against content hash e091c391a919, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

.agents/skills/nextflow-debugging/SKILL.md · 48 lines

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

  1. 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.
  2. Classify the error by exit code / message:

    • 137 / OOM-killed → memory limit. Increase the process memory directive 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.
  3. Add resilience where appropriate: errorStrategy 'retry', maxRetries, and dynamic resources by task.attempt. Don't paper over a real bug with retries.

  4. Reproduce in isolation. Re-run a single component with viash run on the test data before re-running the whole pipeline. Use -resume to avoid recomputing successful tasks.

  5. Validate inputs with validate_spatial_data / analyze_spatial_metadata when 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.

Changes

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.

  1. 6d ago First seen · 48 lines · 53 tokens per session scan A e091c391a919

Subscribe to this mod's changes

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.

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