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Omh Ops Observability Card

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rlaope
omh-ops-observability-card

[omh] Hermes ops observability workflow: prepare an operations command-board for wrapper-safe token, cost, latency, run history, queue, failure-mode, external metric-provider, and service-quality evidence boundaries. Use when the user says: ops-observability-card, observability card, operations command board, ops command board, service quality board, service quality, external metric provider, metric provider.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-ops-observability-card
Stars
2.7K
Forks
194
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 rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Ops Observability Card 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.
  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/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-ops-observability-card .claude/skills/omh-ops-observability-card
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Ops Observability Card 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 Omh Ops Observability Card 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 Omh Ops Observability Card 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 Observability Card

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

Why This Exists

ops-observability-card 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: ops-observability-card show token, cost, latency, supplied Prometheus/Grafana metrics, and missing service-quality evidence for this loop.
  • Expected behavior: Produce prepare_ops_observability_card 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: ops-observability-card claim exact provider billing, healthy SLO, incident closure, or remediation completion from local estimates.
  • 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 run or workflow scope, metric window, failure modes, and cost/latency boundary are named.
  • Local telemetry, provider truth, billing truth, and completion evidence are separate states.
  • Warnings name the next measurement or operator review action.

Recovery Notes

  • If provider metrics are unavailable, report only local metadata and mark provider truth not_observed.
  • If cost or latency looks risky, surface a warning plus the next measurement rather than a completion claim.

Use When

Use when automation, loops, gateway work, executor handoffs, or service operations need a safe command-board for cost, latency, token, history, failure-mode, supplied metric-provider, and service-quality visibility.

Strong routing signals: `ops-observability-card`, `observability card`, `operations command board`, `ops command board`, `service quality board`, `service quality`, `external metric provider`, `metric provider`, `prometheus metrics`, `grafana metrics`, `cost telemetry`, `latency telemetry`, `token telemetry`, `run history`, `loop telemetry`, `failure mode`, `monitor tokens`, `service health`, `slo dashboard`, `비용`, `토큰`, `지연시간`, `관측성`, `운영 지휘판`, `서비스 품질`, `메트릭`, `프로메테우스`, `그라파나`

Catalog Metadata

Category: observability Phase: telemetry-card Quality tier: workflow-surface-gated Reasoning demand: standard

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.
  • When advising what to record per model call, require the five answers - which model, how long, how many tokens in and out, whether it succeeded, and why it failed - with streaming calls adding time-to-first-token; the attribute tiers live in omh-agent-ops-review/references/instrumentation-ladder.md.
  • Aggregate cost at the four levels - per call, per agent run, per session, per user - and name the budget threshold each level checks against before recommending any optimization signal.
  • Never recommend logging raw prompts, responses, or secret values into telemetry; counts, lengths, hashes, and key-set booleans carry the signal without the leak.

Required inputs:

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

Expected outputs:

  • ops-observability-card/v1 card or guidance
  • external_metric_provider/v1 payload contract
  • external_metric_provider_adapter/v1 adapter contract
  • ops_service_quality_board/v1 service-quality board
  • typed service-quality downgrade gaps
  • next action
  • prepared-vs-observed boundary

Artifact expectations:

  • ops-observability-card/v1 metadata-only runtime or wrapper card when recorded
  • external_metric_provider/v1 supplied metric payload when available
  • external_metric_provider_adapter/v1 connector-ready adapter metadata when available
  • ops_service_quality_board/v1 evidence-gated service-quality board

Artifact contracts:

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

  • contract_id: ops_service_quality_board/v1; enforcement_level: executable_validated; consumer_id: validate_ops_service_quality_board

Safety rules:

  • An ops observability card is not billing truth, provider quota truth, live metric-provider access, complete tracing, SLO pass, incident closure, root-cause proof, remediation completion, performance proof, or successful workflow completion 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.

Frequently asked questions

What does the Omh Ops Observability Card AI skill do?

[omh] Hermes ops observability workflow: prepare an operations command-board for wrapper-safe token, cost, latency, run history, queue, failure-mode, external metric-provider, and service-quality evidence boundaries. Use when the user says: ops-observability-card, observability card, operations command board, ops command board, service quality board, service quality, external metric provider, metric provider.

Why use Omh Ops Observability Card on TypingMind?

Because you install it once and use it with any model. Omh Ops Observability Card 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 Omh Ops Observability Card in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-ops-observability-card. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Omh Ops Observability Card?

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 Omh Ops Observability Card?

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

Is the Omh Ops Observability Card AI skill free?

Yes. It is published on GitHub by rlaope 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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