Creator · rlaope
Last updated · Sep 2, 2026
[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 revi
Creator · rlaope
Last updated · Sep 2, 2026
[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 revi
Creator · rlaope
Last updated · Sep 2, 2026
[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 revi
Creator · rlaope
Last updated · Sep 2, 2026
[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 revi
Review then install
Install targets
Codex install prompt
Install the "omh-agent-ops-review" agent skill from https://github.com/rlaope/oh-my-hermes/tree/main/skills/omh-agent-ops-review. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: [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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"rlaope-omh-agent-ops-review","task":"Install omh-agent-ops-review","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Maintenance
fresh
2d since push
Risk
Safe to try
Quality score needs review
GitHub quality
1.3K
78/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
1.3K GitHub stars
Repo activity
1.3K stars, 125 forks
Maintenance
2d since push
License
MIT
Install
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewDo not use when
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Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
Agent should check
Copy prompt
Task: Use omh-agent-ops-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install
Install command: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
LLM text format
/api/skills/rlaope-omh-agent-ops-review/install?format=text
Find alternatives
/api/skills/search?q=omh-agent-ops-review&limit=3
Agent prompt
Use omh-agent-ops-review for this task. Review https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install, then install with: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/rlaope-omh-agent-ops-review
LLM text
/api/registry/manifest/rlaope-omh-agent-ops-review?format=text
Install alias
/api/registry/install/rlaope-omh-agent-ops-review
Recommend
/api/registry/recommend?task=Use%20omh-agent-ops-review%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
GitHub automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS1.3K GitHub stars
Stars/forks activity
INFO1.3K stars, 125 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
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--- name: "omh-agent-ops-review" description: "[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." metadata: hermes: tags: [workflow, oh-my-hermes, operator] category: operator phase: manager-review role: tracker quality_tier: workflow-surface-gated ---
# Agent Ops Review
This is a Hermes-native `agent-ops-review` workflow skill.
## 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.
## Workflow Lane
- Current lane: **Automation and status** (`achievements`, `workspace-audit`, `production-audit`, `automation-blueprint`, `github-event-ops`, `buzz`, `agent-board`, `gateway-intent-card`, `+34 more`) - schedules, status, health, and ops review. - If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.
## 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`, `今何してる`, `现在在做什么`, `qué está pasando`, `qu'est-ce qui se passe`, `was ist los`, `ai agent manager`, `관리자 입장`, `Codex 작업`, `Codex 작업이 어디까지`, `코덱스 작업`, `작업이 어디까지`, `진행됐는지`, `진행되었는지`, `처리량`, `작업 품질`, `진행상황`, `무슨일이노`, `뭔일임`, `무슨 일이야`, `뭐해`, `지금 뭐 하고 있어`, `작업상황 브리핑`, `어디까지 됐어`, `리서치 코딩 리뷰`
## Catalog Metadata
Category: `operator` Phase: `manager-review` Hermes role: `tracker` 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.
Handoff policy:
Keep this as Hermes-facing orchestration guidance first. Prepare executor, connector, gateway, or host-runtime handoff only when the user accepts that next step and observed evidence can be recorded.
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
Preferred harness for this skill: `agent-ops-review`.
```sh omh runtime record --skill agent-ops-review --harness agent-ops-review --status started ```
Record observed delegation results; otherwise return `not_available` or `not_observed`. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence. - Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion.
Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.
Shared product, compatibility, topology, memory, harness, and execution rules: `omh-routing/references/skill-common-rail.md`. Load it when applicable; otherwise name an unavailable capability.
Source provenance
Decision snapshot
1,309 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for omh-agent-ops-review, ready for a manual X post.
omh-agent-ops-review: [omh] Hermes agent ops review workflow: help managers inspect AI-agent progress, blockers, qu... 1.3K stars https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x
Listing + install path for omh-agent-ops-review: https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x Install: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to rlaope but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review/audit)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)rlaope
@rlaope
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
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Install targets
Codex install prompt
Install the "omh-agent-ops-review" agent skill from https://github.com/rlaope/oh-my-hermes/tree/main/skills/omh-agent-ops-review. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: [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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"rlaope-omh-agent-ops-review","task":"Install omh-agent-ops-review","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Maintenance
fresh
2d since push
Risk
Safe to try
Quality score needs review
GitHub quality
1.3K
78/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
1.3K GitHub stars
Repo activity
1.3K stars, 125 forks
Maintenance
2d since push
License
MIT
Install
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewDo not use when
Alternative
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Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
Agent should check
Copy prompt
Task: Use omh-agent-ops-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install
Install command: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
LLM text format
/api/skills/rlaope-omh-agent-ops-review/install?format=text
Find alternatives
/api/skills/search?q=omh-agent-ops-review&limit=3
Agent prompt
Use omh-agent-ops-review for this task. Review https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install, then install with: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/rlaope-omh-agent-ops-review
LLM text
/api/registry/manifest/rlaope-omh-agent-ops-review?format=text
Install alias
/api/registry/install/rlaope-omh-agent-ops-review
Recommend
/api/registry/recommend?task=Use%20omh-agent-ops-review%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
GitHub automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS1.3K GitHub stars
Stars/forks activity
INFO1.3K stars, 125 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: "omh-agent-ops-review" description: "[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." metadata: hermes: tags: [workflow, oh-my-hermes, operator] category: operator phase: manager-review role: tracker quality_tier: workflow-surface-gated ---
# Agent Ops Review
This is a Hermes-native `agent-ops-review` workflow skill.
## 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.
## Workflow Lane
- Current lane: **Automation and status** (`achievements`, `workspace-audit`, `production-audit`, `automation-blueprint`, `github-event-ops`, `buzz`, `agent-board`, `gateway-intent-card`, `+34 more`) - schedules, status, health, and ops review. - If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.
## 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`, `今何してる`, `现在在做什么`, `qué está pasando`, `qu'est-ce qui se passe`, `was ist los`, `ai agent manager`, `관리자 입장`, `Codex 작업`, `Codex 작업이 어디까지`, `코덱스 작업`, `작업이 어디까지`, `진행됐는지`, `진행되었는지`, `처리량`, `작업 품질`, `진행상황`, `무슨일이노`, `뭔일임`, `무슨 일이야`, `뭐해`, `지금 뭐 하고 있어`, `작업상황 브리핑`, `어디까지 됐어`, `리서치 코딩 리뷰`
## Catalog Metadata
Category: `operator` Phase: `manager-review` Hermes role: `tracker` 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.
Handoff policy:
Keep this as Hermes-facing orchestration guidance first. Prepare executor, connector, gateway, or host-runtime handoff only when the user accepts that next step and observed evidence can be recorded.
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
Preferred harness for this skill: `agent-ops-review`.
```sh omh runtime record --skill agent-ops-review --harness agent-ops-review --status started ```
Record observed delegation results; otherwise return `not_available` or `not_observed`. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence. - Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion.
Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.
Shared product, compatibility, topology, memory, harness, and execution rules: `omh-routing/references/skill-common-rail.md`. Load it when applicable; otherwise name an unavailable capability.
Source provenance
Decision snapshot
1,309 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for omh-agent-ops-review, ready for a manual X post.
omh-agent-ops-review: [omh] Hermes agent ops review workflow: help managers inspect AI-agent progress, blockers, qu... 1.3K stars https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x
Listing + install path for omh-agent-ops-review: https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x Install: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
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Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review/audit)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)rlaope
@rlaope
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
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61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsReview then install
Install targets
Codex install prompt
Install the "omh-agent-ops-review" agent skill from https://github.com/rlaope/oh-my-hermes/tree/main/skills/omh-agent-ops-review. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: [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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"rlaope-omh-agent-ops-review","task":"Install omh-agent-ops-review","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Maintenance
fresh
2d since push
Risk
Safe to try
Quality score needs review
GitHub quality
1.3K
78/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
1.3K GitHub stars
Repo activity
1.3K stars, 125 forks
Maintenance
2d since push
License
MIT
Install
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewDo not use when
Alternative
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npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
Agent should check
Copy prompt
Task: Use omh-agent-ops-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install
Install command: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
LLM text format
/api/skills/rlaope-omh-agent-ops-review/install?format=text
Find alternatives
/api/skills/search?q=omh-agent-ops-review&limit=3
Agent prompt
Use omh-agent-ops-review for this task. Review https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install, then install with: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/rlaope-omh-agent-ops-review
LLM text
/api/registry/manifest/rlaope-omh-agent-ops-review?format=text
Install alias
/api/registry/install/rlaope-omh-agent-ops-review
Recommend
/api/registry/recommend?task=Use%20omh-agent-ops-review%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
GitHub automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS1.3K GitHub stars
Stars/forks activity
INFO1.3K stars, 125 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: "omh-agent-ops-review" description: "[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." metadata: hermes: tags: [workflow, oh-my-hermes, operator] category: operator phase: manager-review role: tracker quality_tier: workflow-surface-gated ---
# Agent Ops Review
This is a Hermes-native `agent-ops-review` workflow skill.
## 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.
## Workflow Lane
- Current lane: **Automation and status** (`achievements`, `workspace-audit`, `production-audit`, `automation-blueprint`, `github-event-ops`, `buzz`, `agent-board`, `gateway-intent-card`, `+34 more`) - schedules, status, health, and ops review. - If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.
## 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`, `今何してる`, `现在在做什么`, `qué está pasando`, `qu'est-ce qui se passe`, `was ist los`, `ai agent manager`, `관리자 입장`, `Codex 작업`, `Codex 작업이 어디까지`, `코덱스 작업`, `작업이 어디까지`, `진행됐는지`, `진행되었는지`, `처리량`, `작업 품질`, `진행상황`, `무슨일이노`, `뭔일임`, `무슨 일이야`, `뭐해`, `지금 뭐 하고 있어`, `작업상황 브리핑`, `어디까지 됐어`, `리서치 코딩 리뷰`
## Catalog Metadata
Category: `operator` Phase: `manager-review` Hermes role: `tracker` 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.
Handoff policy:
Keep this as Hermes-facing orchestration guidance first. Prepare executor, connector, gateway, or host-runtime handoff only when the user accepts that next step and observed evidence can be recorded.
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
Preferred harness for this skill: `agent-ops-review`.
```sh omh runtime record --skill agent-ops-review --harness agent-ops-review --status started ```
Record observed delegation results; otherwise return `not_available` or `not_observed`. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence. - Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion.
Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.
Shared product, compatibility, topology, memory, harness, and execution rules: `omh-routing/references/skill-common-rail.md`. Load it when applicable; otherwise name an unavailable capability.
Source provenance
Decision snapshot
1,309 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for omh-agent-ops-review, ready for a manual X post.
omh-agent-ops-review: [omh] Hermes agent ops review workflow: help managers inspect AI-agent progress, blockers, qu... 1.3K stars https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x
Listing + install path for omh-agent-ops-review: https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x Install: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to rlaope but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review/audit)
[](https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)rlaope
@rlaope
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsReview then install
Install targets
Codex install prompt
Install the "omh-agent-ops-review" agent skill from https://github.com/rlaope/oh-my-hermes/tree/main/skills/omh-agent-ops-review. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: [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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"rlaope-omh-agent-ops-review","task":"Install omh-agent-ops-review","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Maintenance
fresh
2d since push
Risk
Safe to try
Quality score needs review
GitHub quality
1.3K
78/100 Quality · 84/100 Trust
Coverage tags
Review notes
Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Review then installGood shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Use as the primary candidate after human or sandbox review.
Stars
1.3K GitHub stars
Repo activity
1.3K stars, 125 forks
Maintenance
2d since push
License
MIT
Install
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
Agent should check
Copy prompt
Task: Use omh-agent-ops-review in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20omh-agent-ops-review%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install
Install command: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/rlaope-omh-agent-ops-review/install
LLM text format
/api/skills/rlaope-omh-agent-ops-review/install?format=text
Find alternatives
/api/skills/search?q=omh-agent-ops-review&limit=3
Agent prompt
Use omh-agent-ops-review for this task. Review https://www.openagentskill.com/api/skills/rlaope-omh-agent-ops-review/install, then install with: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-reviewRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/rlaope-omh-agent-ops-review
LLM text
/api/registry/manifest/rlaope-omh-agent-ops-review?format=text
Install alias
/api/registry/install/rlaope-omh-agent-ops-review
Recommend
/api/registry/recommend?task=Use%20omh-agent-ops-review%20in%20an%20agent%20workflow&limit=3
Agent fit
GitHub automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
GitHub automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.
GitHub adoption
PASS1.3K GitHub stars
Stars/forks activity
INFO1.3K stars, 125 forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Use as the primary candidate after human or sandbox review.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: "omh-agent-ops-review" description: "[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." metadata: hermes: tags: [workflow, oh-my-hermes, operator] category: operator phase: manager-review role: tracker quality_tier: workflow-surface-gated ---
# Agent Ops Review
This is a Hermes-native `agent-ops-review` workflow skill.
## 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.
## Workflow Lane
- Current lane: **Automation and status** (`achievements`, `workspace-audit`, `production-audit`, `automation-blueprint`, `github-event-ops`, `buzz`, `agent-board`, `gateway-intent-card`, `+34 more`) - schedules, status, health, and ops review. - If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.
## 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`, `今何してる`, `现在在做什么`, `qué está pasando`, `qu'est-ce qui se passe`, `was ist los`, `ai agent manager`, `관리자 입장`, `Codex 작업`, `Codex 작업이 어디까지`, `코덱스 작업`, `작업이 어디까지`, `진행됐는지`, `진행되었는지`, `처리량`, `작업 품질`, `진행상황`, `무슨일이노`, `뭔일임`, `무슨 일이야`, `뭐해`, `지금 뭐 하고 있어`, `작업상황 브리핑`, `어디까지 됐어`, `리서치 코딩 리뷰`
## Catalog Metadata
Category: `operator` Phase: `manager-review` Hermes role: `tracker` 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.
Handoff policy:
Keep this as Hermes-facing orchestration guidance first. Prepare executor, connector, gateway, or host-runtime handoff only when the user accepts that next step and observed evidence can be recorded.
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
Preferred harness for this skill: `agent-ops-review`.
```sh omh runtime record --skill agent-ops-review --harness agent-ops-review --status started ```
Record observed delegation results; otherwise return `not_available` or `not_observed`. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence. - Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion.
Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.
Shared product, compatibility, topology, memory, harness, and execution rules: `omh-routing/references/skill-common-rail.md`. Load it when applicable; otherwise name an unavailable capability.
Source provenance
Decision snapshot
1,309 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for omh-agent-ops-review, ready for a manual X post.
omh-agent-ops-review: [omh] Hermes agent ops review workflow: help managers inspect AI-agent progress, blockers, qu... 1.3K stars https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x
Listing + install path for omh-agent-ops-review: https://www.openagentskill.com/skills/rlaope-omh-agent-ops-review?ref=x Install: npx skills add rlaope/oh-my-hermes --skill omh-agent-ops-review
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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Review then install
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsPermission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness