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Omh Harness Session Inventory

CommunityPopular
rlaope
omh-harness-session-inventory

[omh] Hermes harness session inventory workflow: normalize Codex, Claude Code, Hermes, OpenCode, Cursor, MCP host, worktree, and wrapper session metadata into one drift-aware inventory. Use when the user says: harness-session-inventory, harness session inventory, session inventory, session adapter, session adapters, harness sessions, mcp inventory, mcp config inventory.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-harness-session-inventory
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 Harness Session Inventory 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-harness-session-inventory .claude/skills/omh-harness-session-inventory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Harness Session Inventory 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 Harness Session Inventory 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 Harness Session Inventory 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.

Harness Session Inventory

This is an OMH harness-session-inventory workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

harness-session-inventory 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: harness-session-inventory compare Codex, Claude Code, Hermes, MCP configs, and worktrees for drift before we dispatch agents.
  • Expected behavior: Produce prepare_harness_session_inventory 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: harness-session-inventory claim every MCP host loaded and every agent session is healthy from config files alone.
  • 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 inventory scope names the harnesses, sessions, MCP hosts, connector configs, and worktrees being compared.
  • Prepared, observed, missing, stale, and drifted entries are separated before any health or progress claim.
  • The next action says whether to load a host, verify a connector, inspect a worktree, dispatch an executor, or stay blocked.

Recovery Notes

  • If config sources are unavailable, report only the discovered surfaces and mark the missing hosts not_observed.
  • If cleanup, host load, connector execution, or session progress is requested, route to the owning workflow instead of folding it into inventory.

Use When

Use when operators need a cross-harness/session/MCP/worktree inventory and drift summary before claiming any host loaded, connector ran, or agent session progressed.

Strong routing signals: `harness-session-inventory`, `harness session inventory`, `session inventory`, `session adapter`, `session adapters`, `harness sessions`, `mcp inventory`, `mcp config inventory`, `mcp drift`, `harness drift`, `connector drift`, `worktree inventory`, `worktree lifecycle`, `operator inventory`, `control pane inventory`, `codex session inventory`, `claude code session inventory`, `find previous coding session`, `recover coding session`, `previous codex coding session`, `coding session recall`, `세션 인벤토리`, `지난 코딩 세션`, `코딩 세션 복구`, `세션 기억 복구`, `하네스 세션`, `하네스 드리프트`, `MCP 인벤토리`, `MCP 설정 드리프트`, `워크트리 인벤토리`, `커넥터 드리프트`

Catalog Metadata

Category: observability Phase: harness-session-inventory 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.

Required inputs:

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

Expected outputs:

  • harness_session_inventory/v1 card or guidance
  • harness_session_adapter_matrix/v1
  • mcp_inventory_drift_report/v1
  • mcp_tool_name_compatibility/v1
  • worktree_lifecycle_snapshot/v1
  • session_progress_slots/v1
  • next action
  • prepared-vs-observed boundary

Artifact expectations:

  • harness_session_inventory/v1 metadata-only runtime or wrapper card when recorded
  • harness_session_adapter_matrix/v1 with observed, prepared, missing, and stale adapters
  • mcp_inventory_drift_report/v1 with secret-redacted config/source drift only
  • mcp_tool_name_compatibility/v1 with secret-redacted config/source drift only: agents use omh harness mcp-tool-name-compatibility --snapshot <path> for supplied naming metadata; config-only and unsupported adapters stay unobserved, ambiguous names are never selected, and no tools are invoked
  • worktree_lifecycle_snapshot/v1 with merge-conflict and cleanup candidates when observed

Safety rules:

  • A harness session inventory is not host load, MCP tool-call, connector availability, executor dispatch, worktree cleanup, merge-conflict resolution, or session progress 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 Harness Session Inventory AI skill do?

[omh] Hermes harness session inventory workflow: normalize Codex, Claude Code, Hermes, OpenCode, Cursor, MCP host, worktree, and wrapper session metadata into one drift-aware inventory. Use when the user says: harness-session-inventory, harness session inventory, session inventory, session adapter, session adapters, harness sessions, mcp inventory, mcp config inventory.

Why use Omh Harness Session Inventory on TypingMind?

Because you install it once and use it with any model. Omh Harness Session Inventory 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 Harness Session Inventory in TypingMind?

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

Which AI models can use Omh Harness Session Inventory?

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 Harness Session Inventory?

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

Is the Omh Harness Session Inventory 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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