Agentic Ops Orchestrator

Agentic Ops Orchestrator

The single entry skill for operating real-world surfaces as an autonomous agent — the inbox, the repo, the issue tracker, the billing system, the docs drive, the alert stream. It locates the task on the surface × intent map and delegates to one of 16 operator spokes. The cross-cutting discipline every spoke shares — the evidence-first operator loop (resolve the surface → read live state → smallest reversible action → prove it → report exact status) and the secrets/PII guardrails — lives in agentic-ops-core; read it before any live mutation.

Cluster map (spoke → role)

Communication surfaces

• email-ops — mailbox triage, drafting, send, and Sent-folder proof.
• messages-ops — live texts / DMs, one-time-code recovery, thread inspection.
• unified-notifications-ops — collapse scattered alerts into one routed, deduplicated lane.

Code-host & execution surfaces

• github-ops — gh-CLI issue/PR/CI/release/security operations on GitHub.
• git-workflow — branching, commit conventions, merge-vs-rebase, conflict resolution.
• terminal-ops — evidence-first repo execution: run, debug CI, narrow fix, push with proof.

Project-flow & tracker surfaces

• project-flow-ops — triage GitHub↔Linear; public truth (GitHub) vs internal execution (Linear).
• jira-integration — retrieve/update Jira tickets, transitions, comments via MCP or REST.

Knowledge & document surfaces

• knowledge-ops — ingest/sync/dedupe/retrieve across files, MCP memory, vector stores, repos.
• google-workspace-ops — Drive/Docs/Sheets/Slides as one working system (find → inspect → edit).

Revenue surfaces

• customer-billing-ops — per-customer remediation: refunds, churn triage, portal recovery.
• finance-billing-ops — operator revenue truth: MRR, pricing, code-backed billing reality.

Audit & observability surfaces

• automation-audit-ops — inventory which jobs/hooks/connectors/MCPs are live/broken/redundant.
• workspace-surface-audit — audit repo/MCP/connector/env surface; recommend highest-value skills.
• connections-optimizer — prune/grow X + LinkedIn graph with review-first outreach.
• dashboard-builder — turn metrics into a working operator dashboard (Grafana/SigNoz).

Folded spokes (coding-agent runtime, app-connections & usage)

Folded-in operator spokes for driving coding-agent runtimes, wiring external apps, and proving usage/cost. Same evidence-first loop applies — resolve the surface, read live state, smallest reversible action, prove it, report exact status. Load on demand exactly like the spokes above.

Coding-agent execution surfaces

• coding-agent — run Codex CLI / Claude Code / OpenCode / Pi as a background process for programmatic coding runs.
• ai-automation-workflows — choreograph an approved workflow / scheduled automation through the packet + approval gate (no bypass).
• supacode-cli — drive Supacode from the terminal: CLI commands, worktrees, agent runs.
• hyperframes-cli — HyperFrames CLI dev loop (npx hyperframes): scaffold (init), lint/validate, run.

App-connection surfaces

• connect — connect a coding agent to any app: send email, open issues, post messages, update databases.
• connect-apps — connect to named external apps (Gmail, Slack, GitHub) when the user wants to act through them.

Observability & usage surfaces

• langsmith-fetch — debug LangChain/LangGraph agents by pulling execution traces from LangSmith.
• model-usage — summarize per-model usage and cost (Codex / Claude) via the CodexBar CLI local data.
• developer-growth-analysis — analyze recent coding-agent chat history to surface patterns, gaps, and growth.

General-purpose model call

• gemini — Gemini CLI for one-shot Q&A, summaries, and generation.

Routing: "run / background a coding agent" → coding-agent; "run an approved workflow / scheduled automation" → ai-automation-workflows; Supacode terminal → supacode-cli; HyperFrames scaffold/lint/run → hyperframes-cli; "connect my agent to an app / act through Gmail·Slack·GitHub" → connect / connect-apps; "why did my LangChain/LangGraph run do that" → langsmith-fetch; "per-model usage / cost" → model-usage; "analyze my coding history / where am I weak" → developer-growth-analysis; one-shot Gemini ask → gemini.

Routing rules (intent → spoke)

• "Triage / clean my inbox", "draft a reply", "prove it sent" → email-ops.
• "Read my texts / DMs", "find the code" → messages-ops.
• "Alerts are noisy", "one notification policy", "what should interrupt" → unified-notifications-ops.
• "Manage issues/PRs/CI/releases on GitHub" → github-ops; branching / merge / rebase / conflicts → git-workflow; "run / debug / fix / push this repo" → terminal-ops.
• "Should this be a Linear issue?", "audit the PR backlog", GitHub↔Linear coordination → project-flow-ops; Jira tickets / transitions / comments → jira-integration.
• "Save / sync / search my knowledge", dedupe across stores → knowledge-ops; find/edit a Doc/Sheet/Slide, clean a tracker → google-workspace-ops.
• One customer's refund / cancel / billing breakage → customer-billing-ops; revenue snapshot, pricing, "is per-seat real in code" → finance-billing-ops.
• "What automations are live/broken/redundant" → automation-audit-ops; "what can my environment do / set up Claude Code" → workspace-surface-audit; social graph cleanup/growth → connections-optimizer; build a monitoring dashboard → dashboard-builder.

Standard Operating Flow

1. Locate the task: which surface (comms / code-host / project-flow / knowledge / revenue / audit) and which intent (inspect · triage · mutate · prove · audit).
2. Pull the loop from agentic-ops-core before any state change — resolve-surface → read-live → smallest-reversible → prove → exact-status is identical across every spoke.
3. Delegate to the spoke(s). Multi-surface asks fan out in evidence order, not parallel-blind — e.g. "fix the CI failure and tell the team" → terminal-ops (prove the fix) → unified-notifications-ops (route the result). "Audit first" asks (automation-audit-ops, workspace-surface-audit) run before any remediation spoke.
4. Return: chosen spoke(s), the surface(s) touched, the live-state evidence captured, and the exact status word + next action.

Sibling clusters (when the task outgrows operations)

This cluster operates live surfaces; hand off when the task becomes engineering, not ops:

• Deep git/CI internals — beyond github-ops/terminal-ops live operation (pipeline design, build/release architecture, runner/infra config) → devops-infra-orchestrator; service/API/data-layer design behind the repo → backend-architecture-orchestrator.
• Billing/revenue beyond ops truth — finance-billing-ops owns the operator revenue snapshot; the system that produces it (schema, pricing service, data pipelines) belongs to backend-architecture-orchestrator and databases-data-orchestrator.
• Hardening a surface — auth, secrets management, or vulnerability review of the repo/billing/notification stack → security-orchestrator.

Stay in agentic-ops for operating and proving the surface; route out once the work is building it.

Guardrails

See agentic-ops-core. In short: read before you write — resolve the exact surface and inspect live state before any mutation; default to read-only / draft unless a live send/push/refund was explicitly requested; never claim sent / pushed / fixed / refunded without naming the proof; never expose secrets, tokens, or unnecessary PII; separate fact from recommendation; keep one canonical home per fact set; and when the real fix is a different surface (triage, hook policy, product gap), say so instead of forcing the current tool. The cluster's value is provable operations — don't quietly assert state you didn't verify.

Loading spokes on demand

To keep CLI startup context lean, this cluster's spokes are not separately registered as skills — only this orchestrator and its *-core are enumerated. When you route to a spoke named above, load it on demand by reading its file:

~/.agents/skill-clusters/skills/<spoke-name>/SKILL.md (or skills/<spoke-name>/SKILL.md inside the skill-clusters repo).