AugmentClaude

Agent Ops Review

Help managers monitor AI agent progress, blockers, quality metrics, and throughput.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up β€” so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.

    This copies the whole skill folder into ~/.claude/skills/omh-agent-ops-review-rlaope/ β€” the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates β€” they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
    Sign up to copy
  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description β€” no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the β€œWhat this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

[omh] Hermes agent ops review workflow: help managers inspect AI-agent progress, blockers, quality gates, and throughput levers. Use when the user says: agent-ops-review, agent ops review, agent productivity, operator productivity, manager view, quality dashboard, throughput review, agent work quality.

What this skill does

Agent Ops Review

This is an OMH agent-ops-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

agent-ops-review exists so Hermes users can ask for this workflow in chat and receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.

Do Not Use When

  • The request is already handled by a narrower explicit skill with stronger evidence.
  • The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
  • The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.

Examples

Good example:

  • Prompt: agent-ops-review show quality, blockers, and throughput for AI-agent work.
  • Expected behavior: Produce prepare_agent_ops_review with required context, wrapper actions, and not-evidence boundaries.
  • Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.

Bad example:

  • Prompt: agent-ops-review claim Codex finished and CI passed because a handoff exists.
  • Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
  • Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.

Completion Checklist

  • The local command, managed path, config surface, and state artifact inspected are named.
  • Blocking issues, warnings, and optional surfaces are separated.
  • The next repair action is explicit and does not claim a reload or runtime observation.

Recovery Notes

  • If a managed path or config key is missing, route to setup/update repair instead of editing hidden state.
  • If a reload or plugin load was not observed, keep the diagnostic result as local health evidence only.

Use When

Use when Hermes should explain AI-agent work: quality gates, progress, blockers, next actions, and throughput.

Strong routing signals: `agent-ops-review`, `agent ops review`, `agent productivity`, `operator productivity`, `manager view`, `quality dashboard`, `throughput review`, `agent work quality`, `coding progress quality`, `coding progress`, `where is codex`, `what's going on`, `status update please`, `what are you doing`, `what are you working on`, `where are we`, `δ»Šδ½•γ—γ¦γ‚‹`, `ηŽ°εœ¨εœ¨εšδ»€δΉˆ`, `quΓ© estΓ‘ pasando`, `qu'est-ce qui se passe`, `was ist los`, `ai agent manager`, `κ΄€λ¦¬μž μž…μž₯`, `Codex μž‘μ—…`, `Codex μž‘μ—…μ΄ μ–΄λ””κΉŒμ§€`, `μ½”λ±μŠ€ μž‘μ—…`, `μž‘μ—…μ΄ μ–΄λ””κΉŒμ§€`, `μ§„ν–‰λλŠ”μ§€`, `μ§„ν–‰λ˜μ—ˆλŠ”μ§€`, `μ²˜λ¦¬λŸ‰`, `μž‘μ—… ν’ˆμ§ˆ`, `진행상황`, `λ¬΄μŠ¨μΌμ΄λ…Έ`, `λ­”μΌμž„`, `무슨 일이야`, `뭐해`, `μ§€κΈˆ 뭐 ν•˜κ³  μžˆμ–΄`, `μž‘μ—…μƒν™© λΈŒλ¦¬ν•‘`, `μ–΄λ””κΉŒμ§€ 됐어`, `λ¦¬μ„œμΉ˜ μ½”λ”© 리뷰`

Catalog Metadata

Category: operator Phase: manager-review Quality tier: workflow-surface-gated Reasoning demand: light

Quality bar:

  • Name the user-facing workflow objective, required context, next action, and stop condition.
  • Separate prepared guidance from observed platform, runtime, connector, file, memory, or delivery evidence.
  • Expose missing tools, credentials, targets, or observations as user-visible gaps.
  • For instrumentation-audit requests, grade against the tier ladder in omh-agent-ops-review/references/instrumentation-ladder.md: T0 foundation through T5 advanced, with every verdict PASS, FAIL, or PARTIAL and a file or config location attached.
  • Audit coverage in priority order - P0 (telemetry init, LLM-call capture, tool-call capture, error capture) before P1 (tokens, cost attribution, agent identity, multi-agent links) before P2 (memory/RAG spans, human-in-the-loop, evaluation runs) - and rank remediation as quick win (under an hour), medium, or larger.
  • Check the audited setup against the anti-pattern checklist in the same reference; an anti-pattern hit is a finding with its location and fix, never a style remark.

Required inputs:

  • user request
  • target context
  • delivery or status expectation
  • known missing evidence

Expected outputs:

  • agent-ops-review/v1 card or guidance
  • next action
  • prepared-vs-observed boundary

Artifact expectations:

  • agent-ops-review/v1 metadata-only runtime or wrapper card when recorded

Artifact contracts:

This label denotes the machine-enforcement level, not a skill quality score and not an observed evidence state.

  • contract_id: agent_operator_productivity/v1; enforcement_level: executable_validated; consumer_id: validate_agent_operator_productivity_card

Safety rules:

  • An agent ops review card is not source retrieval, executor dispatch, coding progress, implementation, review, verification, CI, merge, platform delivery, provider billing, or live runtime telemetry evidence. If Hermes is the coding owner, summarize hermes_coding_harness/v1 stage, lane owner, next action, and missing evidence.
  • Do not claim connector, gateway, runtime, file generation, memory mutation, or host automation evidence from prepared guidance.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

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