Production incidents dashboard. Reads ECS health, Sentry errors, CI failures. Offers to dispatch fix agents for active fires.
npx skills add https://github.com/davepoon/buildwithclaude --skill ops-fires
Before executing, load available context:
${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/daemon-health.jsoninfra-monitor service status — if not running, pre-gathered infra data may be staleaction_needed is not null → surface it immediately as a potential fire$AWS_ACCESS_KEY_ID / $AWS_PROFILE env varsdoppler secrets get AWS_ACCESS_KEY_ID --plain (if doppler configured in prefs)password_manager_config.query_cmd from preferences$SENTRY_AUTH_TOKEN → Doppler SENTRY_AUTH_TOKEN → vault${CLAUDE_PLUGIN_DATA_DIR}/preferences.json for secrets_manager config to know which vault to query.| Command | Usage | Output |
|---------|-------|--------|
| aws ecs list-services --cluster <name> --query 'serviceArns' | ECS services | ARN list |
| aws ecs describe-services --cluster <name> --services <arn> --query 'services[0].{status:status,running:runningCount,desired:desiredCount}' | Service health | JSON |
| aws logs tail /ecs/<service> --since 1h --format short | ECS logs | Log lines (use with Monitor for live) |
| Command | Usage | Output |
|---------|-------|--------|
| gh run list --limit 20 --json status,conclusion,name,headBranch,createdAt | Recent CI runs | JSON array |
| gh run view <id> --repo <repo> --log-failed | Failed CI logs | Log output |
| Command | Usage | Output |
|---------|-------|--------|
| sentry-cli issues list --project <slug> --status unresolved | Unresolved issues | Issue list |
| curl -H "Authorization: Bearer $SENTRY_AUTH_TOKEN" "https://sentry.io/api/0/projects/<org>/<proj>/issues/?query=is:unresolved" | API fallback when MCP unavailable | JSON array |
If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when dispatching multiple fix agents simultaneously. This enables:
Team setup (only when flag is enabled, dispatch phase):
TeamCreate("fire-fixers")
Agent(team_name="fire-fixers", name="fix-[service]", ...)
If the flag is NOT set, use standard parallel subagents.
${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || echo '{"clusters":[],"error":"infra check failed"}'
${CLAUDE_PLUGIN_ROOT}/bin/ops-ci 2>/dev/null || echo '[]'
${CLAUDE_PLUGIN_ROOT}/bin/ops-external 2>/dev/null || echo '[]'
Analyze the pre-gathered data — including external projects. Then run parallel checks:
gh run list --limit 20 --json status,conclusion,name,headBranch,createdAt 2>/dev/nullauth_expired as HIGH (credential rotation needed), unreachable/degraded as MEDIUM, not_configured as LOW.Classify each issue by severity:
| Severity | Criteria |
| -------- | ------------------------------------------------- |
| CRITICAL | Service down, DB unreachable, auth broken |
| HIGH | Elevated error rate, deploy stuck, CI main broken |
| MEDIUM | Non-critical service degraded, flaky tests |
| LOW | Warning-level, non-urgent |
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
OPS ► FIRES DASHBOARD — [timestamp]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
CRITICAL
[service] — [issue] — [since]
HIGH
[service] — [issue] — [since]
MEDIUM
[service] — [issue] — [since]
ECS HEALTH
[cluster] [service] [desired/running] [status]
CI STATUS
[repo] [branch] [workflow] [status] [last run]
SENTRY (top errors, 24h)
[error] [count] [first seen] [project]
EXTERNAL PROJECTS
[alias] [source] [status] [details — e.g. auth_expired, unreachable]
──────────────────────────────────────────────────────
Use batched AskUserQuestion calls (max 4 options each). Only show relevant actions (e.g., skip dispatch options if no issues found):
AskUserQuestion call 1:
[Dispatch fix agent for [top critical issue]]
[Dispatch fix agent for [second issue]]
[View logs for [service]]
[More...]
AskUserQuestion call 2 (only if "More..."):
[Open Sentry dashboard]
[Open GitHub Actions]
[All clear — nothing to do]
If no fires: show "ALL SYSTEMS OPERATIONAL" with last-checked timestamps.
When user selects to fix an issue, use AskUserQuestion to confirm the scope before dispatching:
Dispatch fix agent for: [issue title]
Severity: [CRITICAL/HIGH/MEDIUM]
Repo: [repo]
Error: [brief description]
The agent will:
- Investigate root cause in [repo]
- Create feature branch with fix
- Open PR for review
[Dispatch agent] [Show me the logs first] [Skip — I'll fix manually]
On confirmation, spawn an Agent with:
Use the agents/infra-monitor.md agent definition for infra issues.
If $ARGUMENTS contains a project alias, filter to that project's services only.
Use Monitor to stream ECS task logs or GitHub Actions runs when investigating fires:
Monitor(command: "aws logs tail /ecs/<service> --follow --since 5m")
Use TaskCreate for each active fire. Update with TaskUpdate as fires are investigated/fixed/escalated.
When diagnosing fires, use WebFetch to check AWS status page (https://health.aws.amazon.com/health/status), Vercel status, or third-party API status pages.
Use WebSearch to find if the error pattern matches a known AWS/infrastructure issue (e.g., "ECS task stopped CannotPullContainerError" → known ECR throttling).
Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.
Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.
Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent analysis
Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation, optimization, publication figures, sensor/time-series data, citation lookup, and common scientific libraries. Use when the user asks for MATLAB code, scientific Python, data analysis, plots, simulations, formulas, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.
> Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".
> Rewrite and optimize existing blog posts for Google SEO (May 2026 Core Update, March 2026 core/spam context, June 2026 spam context, E-E-A-T) and AI citation visibility as one SEO discipline. Full rewrite for both Google rankings AND AI citations. For AI-citation-only audit (no Google work), use blog-geo instead. Replaces fabricated statistics with sourced data, applies answer-first formatting, adds Pixabay/Unsplash images, generates built-in SVG charts, validates Article-priority schema, performs AI content detection, adds citation capsules and information gain markers, and updates freshness signals. Works with any blog format (MDX, markdown, HTML). Use when user says "rewrite blog", "optimize blog", "update blog", "improve blog", "fix blog", "refresh blog post", "blog optimization".
> Write new blog articles from scratch optimized for Google rankings and AI citations. Generates full articles with template selection, answer-first formatting, Key Takeaways summary box, information gain markers, citation capsules, sourced statistics, Pixabay/Unsplash images, built-in SVG chart generation, optional FAQ sections, internal linking zones, and proper heading hierarchy. Supports MDX, markdown, and HTML output. Use when user says "write blog", "new blog post", "create article", "write about", "draft blog", "generate blog post".
> Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all load-bearing claims (statistics, product or policy claims, ranking and comparative claims, named sources), validates cited URLs before fetching, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".
Take davepoon/ops-fires from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.