Ops Fires logo

Ops Fires

Organization
Lifecycle-Innovations-Limited
ops-fires

OPS on-demand: This skill should be used when the user asks to "production fires", "what is on fire"…

Overview

PublisherLifecycle-Innovations-Limited
Repositoryclaude-ops
Skill nameops-fires
Stars
543
Forks
64
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Lifecycle-Innovations-Limited on GitHub. Read the source before you install it.

Installation

Install the Ops Fires AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-fires
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Lifecycle-Innovations-Limited/claude-ops.git /tmp/claude-ops
mkdir -p .claude/skills
cp -r /tmp/claude-ops/claude-ops/skills/ops-fires .claude/skills/ops-fires
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ops Fires in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ops Fires on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ops Fires is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OPS ► FIRES

Load ops-rules before acting. Public repo (no personal data). Outbound: one draft → one approval → one send. If AskUserQuestion / Workflow are missing, follow Rule 10 in ops-rules (Hermes: numbered options / two-turn Telegram card; delegate_task).

Runtime Context

Before executing, load available context:

  1. Daemon health: Read ${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/daemon-health.json

    • Check infra-monitor service status — if not running, pre-gathered infra data may be stale
    • If action_needed is not null → surface it immediately as a potential fire
  2. Secrets: AWS credentials are required for ECS/CloudWatch queries.

    Secret Resolution

    • First: check $AWS_ACCESS_KEY_ID / $AWS_PROFILE env vars
    • Then: doppler secrets get AWS_ACCESS_KEY_ID --plain (if doppler configured in prefs)
    • Then: use password_manager_config.query_cmd from preferences
    • Sentry token: $SENTRY_AUTH_TOKEN → $SENTRY_TOKEN → Doppler claude-ops/prd/SENTRY_AUTH_TOKEN. Resolved by bin/ops-sentry; no action needed here.
    • Sentry org and region: preferences.json .partner_registry.sentry.{org,region_url}. Sentry is region-sharded — issues are served by the org's own region host (e.g. https://us.sentry.io), not sentry.io, and querying the wrong region returns an empty list rather than an error. Omit org and the script discovers the first org the token can see.
  3. Preferences: Read ${CLAUDE_PLUGIN_DATA_DIR}/preferences.json for secrets_manager config to know which vault to query.

CLI/API Reference

aws CLI

CommandUsageOutput
aws ecs list-services --cluster <name> --query 'serviceArns'ECS servicesARN list
aws ecs describe-services --cluster <name> --services <arn> --query 'services[0].{status:status,running:runningCount,desired:desiredCount}'Service healthJSON
aws logs tail /ecs/<service> --since 1h --format shortECS logsLog lines (use with Monitor for live)

gh CLI (GitHub)

CommandUsageOutput
gh run list --limit 20 --json status,conclusion,name,headBranch,createdAtRecent CI runsJSON array
gh run view <id> --repo <repo> --log-failedFailed CI logsLog output

sentry-cli / Sentry API

CommandUsageOutput
sentry-cli issues list --project <slug> --status unresolvedUnresolved issuesIssue list
bin/ops-sentry [org]Pre-gather path (already inlined below)JSON {org,issues,error}
OPS_SENTRY_PERIOD=7d OPS_SENTRY_LIMIT=25 bin/ops-sentryWiden the window or capJSON
curl -H "Authorization: Bearer $SENTRY_AUTH_TOKEN" "https://us.sentry.io/api/0/organizations/<org>/issues/?query=is:unresolved"Manual probe (note: region host, org-scoped)JSON array

Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when dispatching multiple fix agents simultaneously. This enables:

  • Fix agents share findings (e.g., API agent discovers DB is the root cause → infra agent pivots to DB fix)
  • You can prioritize: "CRITICAL ECS issue first, then CI failures"
  • Real-time progress: agents report as they find root causes, you can merge fixes in optimal order

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.

Pre-gathered infrastructure data

${CLAUDE_PLUGIN_ROOT}/bin/ops-infra 2>/dev/null || echo '{"clusters":[],"error":"infra check failed"}'

FinOps dashboard — open anomalies

Live anomaly feed from finops-dashboard (spend spikes, idle services, expired credits, drift detections). High-severity items belong in the FIRES table alongside infra outages. Falls open to [] if the dashboard isn't configured.

${CLAUDE_PLUGIN_ROOT}/scripts/finops-bridge.sh anomalies high 2>/dev/null || echo "[]"

CI failures (last 24h)

${CLAUDE_PLUGIN_ROOT}/bin/ops-ci 2>/dev/null || echo '[]'

Sentry — unresolved issues (last 24h)

Pre-gathered so Sentry is never skipped. Sorted by event frequency. An error field that is not null means the probe itself failed (missing or expired token, unreachable API) — report that as a gap, and do NOT read an empty issues list as "no errors in production".

${CLAUDE_PLUGIN_ROOT}/bin/ops-sentry 2>/dev/null || echo '{"org":null,"issues":[],"error":"sentry probe failed"}'

External projects health

${CLAUDE_PLUGIN_ROOT}/bin/ops-external 2>/dev/null || echo '[]'

Home automation (only if home_automation is configured in $PREFS_PATH)

if jq -e '.home_automation' "${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json" >/dev/null 2>&1; then
  ${CLAUDE_PLUGIN_ROOT}/bin/ops-home snapshot 2>/dev/null || echo '{"configured":true,"error":"home probe failed"}'
else
  echo '{"configured":false}'
fi

Your task

Analyze the pre-gathered data — including external projects. Then run parallel checks:

  1. ECS health — parse infra data for unhealthy services, stopped tasks, failed deployments.
  2. Sentry — parse the pre-gathered Sentry data above; it is always present, so never report Sentry as unchecked. Classify by blast radius: an issue affecting many users or growing fast is HIGH, a warning-level issue with a handful of events is LOW. Only reach for mcp__plugin_sentry_sentry__get_sentry_resource (stack trace, breadcrumbs) or analyze_issue_with_seer when you are about to dispatch a fix agent for that issue. If the probe returned a non-null error, say Sentry could not be read and why — an empty list is not proof production is clean.
  3. CI — parse CI data for failing pipelines, broken main/dev branches.
  4. GitHub Actions — gh run list --limit 20 --json status,conclusion,name,headBranch,createdAt 2>/dev/null
  5. External projects — parse ops-external data. Flag auth_expired as HIGH (credential rotation needed), unreachable/degraded as MEDIUM, not_configured as LOW.
  6. Home automation (only if home snapshot returned configured:true) — classify Homey incidents:
    • Active critical alarm (smoke / water leak / security breach) → P0 / CRITICAL — cross-reference /ops:ops-home alarm for details.
    • Major device offline (gateway, hub, primary thermostat) → P1 / HIGH.
    • Energy spike > 3× 7-day baseline → P2 / MEDIUM — cross-reference /ops:ops-home status. If snapshot returned configured:false, skip silently.

Classify each issue by severity:

SeverityCriteria
CRITICALService down, DB unreachable, auth broken
HIGHElevated error rate, deploy stuck, CI main broken
MEDIUMNon-critical service degraded, flaky tests
LOWWarning-level, non-urgent

Output format

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 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 (unresolved, 24h — by event count)
[short_id] [title truncated] [events]ev [users]u [project] [last seen]
[If the probe returned an error, print that instead of an empty section]

EXTERNAL PROJECTS
[alias] [source] [status] [details — e.g. auth_expired, unreachable]

HOME (only if `home_automation` is configured)
[alarm/device] [type — smoke/water/security/offline/energy] [severity] [since]
[If `configured:false`, omit this section entirely]

──────────────────────────────────────────────────────

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.


Pre-dispatch staleness check (MANDATORY)

The pre-gathered CI data is cached and may be minutes-to-hours old. Before dispatching ANY fix agent, verify the failure is still red on its branch HEAD. This is defense-in-depth: even after the bin/ops-ci "current-state" filter (which only emits workflows whose latest run on a tracked branch is failing), a fix may have landed in the seconds since the cache was written. Dispatching to a self-resolved fire wastes Sonnet quota — typically 50–150k tokens per agent before it figures out there's nothing to fix.

For each fire the user selects:

bash
gh run list --repo "$REPO" --workflow "$WORKFLOW" --branch "$BRANCH" --limit 1 \
  --json conclusion,databaseId,createdAt --jq '.[0]'
  • If conclusion == "success" → SKIP. Mark task completed with metadata {resolution: "self-resolved-pre-dispatch"}. Do NOT spawn agent.
  • If conclusion == "failure" → proceed to dispatch.
  • If conclusion == null (in_progress) → wait 30s, recheck once, then proceed if still null.

For workflows scoped only to PRs (no main/dev runs), check the PR's combined CI status instead: gh pr checks <num> --repo "$REPO" --json bucket,name.

Dispatch fix agent

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:

  • The error details and logs
  • Access to the relevant repo
  • Instruction to create a feature branch, fix, and open a PR
  • Report back when done or blocked

Use the agents/infra-monitor.md agent definition for infra issues.

If $ARGUMENTS contains a project alias, filter to that project's services only.


Native tool usage

Monitor — live service health

Use Monitor to stream ECS task logs or GitHub Actions runs when investigating fires:

Monitor(command: "aws logs tail /ecs/<service> --follow --since 5m")

Tasks — incident tracking

Use TaskCreate for each active fire. Update with TaskUpdate as fires are investigated/fixed/escalated.

WebFetch — status pages

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.

WebSearch — known outage patterns

Use WebSearch to find if the error pattern matches a known AWS/infrastructure issue (e.g., "ECS task stopped CannotPullContainerError" → known ECR throttling).


Credential Expiry & Rate Limit Warnings (Phase 16)

The ops-daemon surfaces two additional fire categories in daemon-health.json:

  • credential_warnings — tokens/keys expiring within 7 days OR API keys older than 180 days. Fed by offline inspection of preferences.json (*_expires_at, *_created_at fields). No live API calls are made to validate credentials.
  • rate_limit_warnings — integrations currently at ≥80% of their quota window. Fed by counters in rate-limits.json. Resets automatically when the window rolls over.

/ops:fires lists both alongside Sentry / infra / CI issues. Push notifications are dispatched by the daemon on the first crossing of the threshold — not re-sent until the next day (credentials) or window rollover (rate limits).


Ledger Integration

CLAIM_KEY: sentry:issue:<short_id> (e.g. sentry:issue:MY-PROJECT-1A2B)

For non-Sentry fires (infra, CI, credential expiry), use:

  • CI failure: ci:run:<repo>:<run_id>
  • Credential expiry: credential:expiry:<service>

Pre-flight skip-check

bash
CLAIM_KEY="sentry:issue:<short_id>"
ledger query --claim-key "$CLAIM_KEY" --since=-PT24H

If in_progress or done exists, skip the issue. If awaiting_user exists, surface it as "fix already staged — needs your decision."

Claim + resolve

bash
# Claim when beginning to investigate/fix
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "fix" \
  --status "in_progress" \
  --title "Fire: <issue title>" \
  --ttl-sec 7200

# Resolve after fix is applied or escalated
ledger write \
  --claim-key "$CLAIM_KEY" \
  --kind "fix" \
  --status "done" \
  --title "Fire: <issue title>" \
  --context "fixed: <brief resolution> | escalated: <reason>"

Frequently asked questions

What does the Ops Fires AI skill do?

OPS on-demand: This skill should be used when the user asks to "production fires", "what is on fire"…

Why use Ops Fires on TypingMind?

Because you install it once and use it with any model. Ops Fires is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ops Fires in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-fires. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ops Fires?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ops Fires?

As many as you like. As long as a model supports skills, you can use Ops Fires with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ops Fires AI skill free?

Yes. It is published on GitHub by Lifecycle-Innovations-Limited under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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