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X Bug Triage

Free

Automate bug triage from X/Twitter complaints.

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Free · Opens the source repo

What X Bug Triage does

X Bug Triage is a skill designed to streamline the process of detecting and managing bugs reported through public complaints on X (formerly Twitter). By automating the intake of complaints, this skill classifies and clusters them into relevant bug families, scans repositories for corroborating evidence, and efficiently routes issues to the appropriate owners. This is particularly useful for product teams who need to stay ahead of potential issues by monitoring social signals about their products.

The skill operates through a series of steps that begin with ingesting mentions from X, normalizing the data, and matching it against existing bug clusters. It utilizes a structured approach to classify and score the reliability of reports, ensuring that only the most relevant information is processed. This helps in creating a clear view of the product's health based on real-time feedback from users.

Once the bugs are triaged, the skill provides options for interactive review directly in the terminal, allowing users to take actions such as filing issues, merging reports, or escalating severity. The results can also be delivered to a Slack channel for team visibility, making collaboration on bug resolution more efficient. This skill is ideal for development teams looking to enhance their bug-tracking processes and respond rapidly to user-reported issues.

When to use it

Use this skill when you need to monitor product health through social media complaints and want to automate the triage process.

When not to use it

This skill may not be suitable for teams that do not use X/Twitter as a source for bug reports or those who prefer manual triage methods.

What you can build with it

Monitoring Product Health

Use this skill to keep track of user complaints on X/Twitter and proactively address bugs before they escalate.

Automating Bug Reports

Automate the process of triaging bugs reported on social media, reducing manual effort and increasing efficiency.

Collaborative Issue Management

Integrate results into your team's workflow by delivering summaries to Slack for collaborative review and action.

How to install X Bug Triage

View source

1. Install with the skills CLI

npx skills add jeremylongshore/claude-code-plugins-plus-skills/x-bug-triage --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by jeremylongshore

X Bug Triage

Closed-loop bug triage from public X/Twitter complaints to clustered, evidence-backed GitHub issues.

Overview

Product teams learn about bugs from X/Twitter hours before internal monitoring catches them. This skill automates the pipeline: ingest complaints, classify and cluster them by bug family, scan repos for corroborating evidence, route to owners, and file issues — all with human confirmation gates. Results display directly in the terminal with optional Slack delivery for team review.

Prerequisites

  • X API bearer token configured at ~/.claude/channels/x-triage/.env
  • SQLite database initialized (bun run db:migrate)
  • config/approved-accounts.json and config/approved-searches.json populated
  • GitHub CLI (gh) for issue filing

Verify environment before starting:

!test -f data/triage.db && echo "DB ready" || echo "Run: bun run db:migrate"
!test -f config/approved-accounts.json && echo "Accounts configured" || echo "Missing: config/approved-accounts.json"

Instructions

Step 1: Intake

  1. Resolve account username to ID: mcp__triage__resolve_username
  2. Fetch mention timeline: mcp__triage__fetch_mentions
  3. Run approved searches: mcp__triage__search_recent
  4. Cross-reference mentions with search results for completeness
  5. Hydrate conversation threads for posts with conversation_id: mcp__triage__fetch_conversation
  6. Fetch quote tweets for high-engagement posts: mcp__triage__fetch_quote_tweets

After intake completes:

  1. Call assessFreshness() from lib/freshness.ts with the combined post set and the requested window boundaries. If date_confidence is "low" or "medium", pass the warning string to the display step for rendering.
  2. Collect all DegradationReport objects from intake tool responses. Call buildSourceStatusReport() from lib/source-status.ts to aggregate into a SourceStatusReport. Pass to the display step for rendering between the header and cluster list.

Step 2: Normalize

For each ingested post:

  • Parse into BugCandidate (all 33 fields) using lib/parser.ts
  • Classify into 12 categories using lib/classifier.ts
  • Redact PII (6 types) using lib/redactor.ts
  • Score reporter reliability (4 dimensions) using lib/reporter-scorer.ts
  • Tag reporter_category from config/approved-accounts.json

Step 3: Match Existing Clusters

  • Load active clusters from DB
  • Load active overrides and suppression rules
  • For each candidate, compute bug signature and match against existing clusters at >=70% overlap
  • Family-first guard: different families NEVER cluster

Step 4: Create/Update Clusters

  • New matches: create cluster with initial severity "low"
  • Existing matches: update report_count, last_seen, sub_status
  • Resolved matches: set state to "open", sub_status to "regression_reopened"
  • Suppressed candidates: skip with audit log

Step 5: Repo Scan

For each cluster (top 3 repos per cluster):

  • mcp__triage__search_issues — Match symptoms/errors
  • mcp__triage__inspect_recent_commits — 7-day commit window
  • mcp__triage__inspect_code_paths — Affected paths
  • mcp__triage__check_recent_deploys — Recent releases

Assign evidence tiers (1-4) per evidence-policy.md.

Load evidence tier definitions:

!cat ${CLAUDE_SKILL_DIR}/references/evidence-policy.md

Step 6: Route Ownership

For each cluster, use strict 6-level precedence:

  1. mcp__triage__lookup_service_owner
  2. mcp__triage__lookup_oncall
  3. mcp__triage__parse_codeowners
  4. mcp__triage__lookup_recent_assignees
  5. mcp__triage__lookup_recent_committers
  6. Fallback mapping from config

Apply routing overrides from prior runs. Flag stale signals (>30 days).

Load routing precedence rules:

!cat ${CLAUDE_SKILL_DIR}/references/routing-rules.md

Step 7: Evaluate Severity + Escalation

Compute severity (low/medium/high/critical) based on:

  • Report velocity, data loss signals, security/privacy, auth/billing lockout
  • Cross-surface failure, enterprise impact, reproducibility quality
  • Apply severity overrides from prior runs

Load escalation trigger definitions:

!cat ${CLAUDE_SKILL_DIR}/references/escalation-rules.md

Step 8: Display Results

Display triage results directly in the terminal as formatted markdown:

  • Severity icons: red_circle critical/high, yellow_circle medium, green_circle low
  • Top 5 clusters by severity (or all if <=5)
  • Per cluster: report count, severity, status, assigned team, top evidence tier
  • Available commands listed at the bottom

Step 9: Optional Slack Delivery

Check if claude-code-slack-channel plugin is available via mcp__slack__reply tool. If available, also deliver summary to Slack. If not, skip — terminal output is sufficient. Not an error.

Step 10: Interactive Review

Accept review commands from the user in the terminal. Parse via mcp__triage__parse_review_command.

CommandAction
details <#>Display full cluster detail
file <#>Generate draft via mcp__triage__create_draft_issue
dismiss <#> <reason>Create noise_suppression override
merge <#> <issue>Link cluster to existing issue
escalate <#>Raise severity
monitor <#>Set cluster to monitoring
snooze <#> <duration>Temporarily suppress
split <#>Split cluster
reroute <#>Change routing
full-reportDisplay all clusters
confirm file <#>File via mcp__triage__confirm_and_file

After each command executes successfully, display the confirmation message from formatActionConfirmation() (in mcp/triage-server/lib.ts). This provides immediate user feedback for all review actions.

Load override and memory policy when processing review commands:

!cat ${CLAUDE_SKILL_DIR}/references/review-memory-policy.md

Step 11: Persist Learning

  • All overrides stored in DB for future runs
  • Audit log captures all actions (12 event types)
  • Suppression rules created from dismiss commands
  • Issue-family links created from file/merge commands

Output

Terminal markdown summary with severity-ranked clusters, evidence tiers, team assignments, and interactive command menu. Optionally mirrored to Slack.

Examples

/x-bug-triage @AnthropicAI --window 24h

Produces cluster summary, then user interacts:

> details 1
> file 2
> dismiss 3 noise
> confirm file 2

Error Handling

ErrorCauseSolution
X_BEARER_TOKEN not setMissing env configCreate ~/.claude/channels/x-triage/.env
Rate limited (429)X API quota exhaustedAutomatic retry with backoff, degrades gracefully
No clusters foundNo bug-like posts in windowWiden --window or check approved-searches.json
Routing uncertainNo routing signalsManual assignment required — flagged in output
Duplicate detectedIssue already filedUse merge command instead of file

Resources

References: ${CLAUDE_SKILL_DIR}/references/

Frequently asked questions about X Bug Triage

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