Infrahub Analyzing Diagnostics

Infrahub Diagnostics Analyzer

Overview

infrahub-collecting-diagnostics produces a bundle; this skill is the next step — it reads that bundle and turns raw logs into a triage report. It parses bundle_information.json, sweeps every service's logs for error signals (tracebacks, ERROR/CRITICAL lines, panics, OOM kills, restart evidence), correlates related errors into incidents, and searches existing opsmill/infrahub GitHub issues so the user learns whether their crash is already known before anyone files anything.

The output is a findings report grounded in bundle evidence — not a fix. The skill is strictly read-only toward the deployment: it reads files from an already-collected bundle and runs GitHub searches; it never touches the running Infrahub instance and never applies remediation.

When to Use

Trigger this skill when the user says things like:

• "I collected a bundle — can you tell me what's wrong?"
• "Analyze these Infrahub logs / this bundle"
• "What do the tracebacks in the bundle mean?"
• "Is this crash a known Infrahub issue?"
• "Why did the task-worker keep restarting?"

Do not trigger when:

• No bundle exists yet — hand off to infrahub-collecting-diagnostics to collect one first. Do not hand-roll log collection here.
• The user wants to file a bug or feature request (use infrahub-reporting-issues)
• The user is asking operational questions about live data (use infrahub-analyzing-data)

Workflow

Follow these steps in order.

1. Ask for the bundle location (user-gate)

Ask the user where the bundle is — never scan the filesystem for it or assume the collector's default output directory. A machine often holds several bundles, and picking the wrong one produces a confident report about the wrong incident. Mentioning the default as a hint is fine (./infrahub_bundles/ when --output-dir wasn't set), but the user names the path. Skip the question only when the user already gave a path or pasted the bundle contents. Once given, confirm it looks like an infrahub-collect bundle — a bundle/ directory containing bundle_information.json. If there is no bundle at all, stop and hand off to infrahub-collecting-diagnostics; do not scrape docker compose logs/kubectl logs as a substitute. See rules/workflow-ask-bundle-location.md.

2. Read the manifest first, then anchor the deployment context

Read bundle/bundle_information.json before opening any log. It records what was collected, what failed, and for which deployment. Collection failures are findings in their own right — a service whose logs could not be collected is often the service that is down. See rules/workflow-manifest-first.md.

Then establish the deployment context — running Infrahub version, topology (Compose or Kubernetes), replica counts — and open the report with it. The version is what later turns a matched GitHub issue into a conclusion ("already fixed in X.Y.Z — upgrade" vs "already running the fix — possible regression"). See rules/workflow-deployment-context.md.

3. Sweep for error signals

Scan every service directory under bundle/logs/ — not just the server — for the signal classes in rules/triage-error-signals.md: Python tracebacks, ERROR/CRITICAL log lines, panics, OOM kills, and connection failures. Treat any *.previous.log file as restart evidence and read its tail — the pre-restart log usually holds the crash cause (rules/triage-restart-evidence.md). reference.md has ready-made grep patterns per signal class.

If the manifest shows a benchmark was collected (create --benchmark), evaluate it alongside the logs — the single-CPU score and the storage IOPS of the Neo4j/PostgreSQL volumes often decide whether a slowness symptom is a software issue or an undersized host. If it's absent and the symptom is performance-shaped, the report must recommend a next bundle with --benchmark. See rules/triage-benchmark-results.md.

4. Correlate into incidents

Group the raw signals by timestamp and causal chain into incidents — one incident per underlying problem, with root errors distinguished from cascade errors in downstream services. A database OOM at 14:02 followed by server connection errors at 14:02+ is one incident, not two. See rules/correlate-into-incidents.md.

5. Match against existing GitHub issues

For each incident backed by a traceback or a distinctive error message, build a search key from its stable parts (exception class, normalized message, innermost Infrahub frame — variable IDs, branch names, and hostnames stripped) and search:

gh search issues --repo opsmill/infrahub "<stable keywords>"

The default search covers open and closed issues — do not pass --state, which would hide one half.

Present the top matches with title, state, and URL — which is all gh search issues returns. The fix version is not in that output, so open a promising match for it and compare it against the deployment context from step 2; when the issue names no version, say so and leave the upgrade question open. See rules/match-stable-search-keys.md for key construction and fallbacks when gh is unavailable, and reference.md for the command that reads a match, plus the known failure patterns worth checking before searching — several common symptoms have well-understood causes that make the search targeted instead of generic.

6. Report findings

Write the findings report: one section per incident, each with severity, the evidence (bundle file paths plus quoted excerpts), the correlation reasoning, and any matching GitHub issues. Every claim must trace back to a quoted bundle line; unknowns are stated as unknowns (rules/report-evidence-per-finding.md). Include in the open questions whether the symptom reproduces on demand and when it last did — a reproduced timestamp sharpens the incident window and tells the next bundle what to capture. The report recommends next steps but applies none — no restarts, no config edits (rules/scope-read-only-analysis.md).

7. Hand off

Close with the right hand-off for what was found:

• A matching open GitHub issue → point the user to it; commenting with their reproduction goes through infrahub-reporting-issues.
• No match and the user wants to file → hand off to infrahub-reporting-issues. Never run gh issue create from this skill.
• Deeper expert help needed → the bundle plus this report go to OpsMill support, following the review-before-sharing gate from infrahub-collecting-diagnostics. If the findings suggest a reproducer is needed, propose a minimal reproducible example first; a full backup (--include-backup) is the last resort, not the default ask.

See rules/cross-link-skill-boundaries.md.

Rule Categories

See rules/_sections.md for the full index.

Supporting References

• reference.md — signal-class grep patterns, per-service log formats, manifest fields, and gh search recipes. Read in steps 3-5.
• examples.md — end-to-end example: bundle excerpts in, findings report out.
• ../infrahub-collecting-diagnostics/reference.md — the authoritative bundle layout produced by infrahub-collect.
• ../infrahub-common/rules/workflow-information-priority.md -- Skill content first; how to consult docs.infrahub.app on a genuine gap.

Anti-patterns

• Deducing the bundle location. No filesystem scans, no "newest directory wins", no assuming ./infrahub_bundles/. Ask; the user names the path.
• Diagnosing without evidence. Every finding cites a bundle path and a quoted excerpt. A plausible story that no log line supports is speculation, and it must be labeled as such.
• Reporting a flat error list. Fifty connection errors downstream of one database OOM are one incident. Correlate before reporting.
• Searching GitHub with volatile tokens. Branch names, UUIDs, hostnames, and timestamps make searches return nothing. Strip them; search the stable parts.
• Fixing instead of analyzing. No restarts, no docker/kubectl mutations, no config edits. Recommendations go in the report; actions go to the user or to OpsMill support.
• Filing a GitHub issue from this skill. That is infrahub-reporting-issues. Cross-link, don't duplicate its routing and sanitization.
• Re-collecting by hand when the bundle is incomplete. A partial bundle is expected on a degraded deployment; the manifest records the gap. If more data is genuinely needed, hand back to infrahub-collecting-diagnostics.