Agent skill

Omh Model Optimization

by rlaope in rlaope/oh-my-hermes

[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement…

MITAuto-check passedBusiness, Finance & HR

Install Omh Model Optimization

skills CLI
$ npx skills add rlaope/oh-my-hermes --skill omh-model-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rlaope/oh-my-hermes omh-model-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/omh-model-optimization .claude/skills/omh-model-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
omh-model-optimization
GitHub stars
3.2k
Token cost
~2.3k tokens
SKILL.md length
1,147 words
Files
1
Skills in repo
143
Repo updated
First seen
Licence
MIT

At a glance

[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement…

  • The user says: model-optimization
  • SKILL.md covers Why This Exists, Do Not Use When, Examples and Completion Checklist, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Model optimization

What it does

Omh Model Optimization is an agent skill from rlaope/oh-my-hermes. [omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current. Use when the user says: model-optimization, model optimization, optimize for model, onboard new model, calibrate new model, new model calibration, model calibration.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Performance reviews. The repository describes itself as: All in one plugin for Hermes Agent ⚚ the coding intelligence, a long-term memory system and model optimized workflow packages. The licence is MIT.

When your agent uses it

  • The user says: model-optimization
  • Model optimization
  • Optimize for model
  • Onboard new model

Example prompts

  • “/omh-model-optimization”

What it can do on your machine

Read from SKILL.md and the folder at commit f772a94. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Omh Model Optimization loads about 2.3k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 1,147 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~110
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rlaope/oh-my-hermes at commit f772a94, republished under its MIT licence (© rlaope). 1,147 words, ~2,287 tokens.

Download SKILL.mdSave it as .claude/skills/omh-model-optimization/SKILL.md (or your agent's skills folder).
name
omh-model-optimization
description
[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current. Use when the user says: model-optimization, model optimization, optimize for model, onboard new model, calibrate new model, new model calibration, model calibration.

Model Optimization

This is a Hermes-native model-optimization workflow skill.

Why This Exists

model-optimization exists so a new model release triggers one repeatable, evidence-ordered process instead of ad-hoc edits: recognition proves what the router sees, official-first research separates contracts from folklore, trait-to-counter keeps calibrations concrete, and the measurement close keeps them honest.

Do Not Use When

  • The user wants their own machine's model routing configured or providers connected; use model-setup.
  • The goal is measurable performance of an application or system, not model handling; use ultraperf.
  • The user wants benchmark-superiority or provider-readiness claims without measurements.

Examples

Good example:

  • Prompt: GLM 5.3 and 5.3 Flash just shipped; check what we should optimize for them.
  • Expected behavior: Probe recognition for both ids, verify family coverage, research the official thinking/tool contract plus community harness handling with labeled sources, draft version-aware trait-to-counter calibration, propose chain placement distinguishing the Flash sibling from the highspeed tier, and name the benchmark pair as the measurement close.
  • Why: A new generation of a known family needs the whole process, not just a chain edit.

Bad example:

  • Prompt: Just say the new model is the best and route everything to it.
  • Expected behavior: Refuse the superiority claim, run the process, and place routing only with owner-approved config or repo changes backed by labeled sources.
  • Why: Unmeasured superiority claims and blanket rerouting are exactly what the process exists to prevent.

Completion Checklist

  • Recognition probe output exists for every new id, and the family label is the expected one.
  • Every research finding is labeled official, official-client, observed, or community with its source kept.
  • The calibration draft counters named traits and marks version-specific rules as such.
  • Routing and pricing changes name their surface (operator config vs repo change) and their approval state.
  • The measurement plan names the benchmark pair, or the recorded reason none can run, and the worse-measured-calibration rule is stated.

Recovery Notes

  • If official docs and community reports conflict, ship the official contract and record the community finding as an unconfirmed counter-signal.
  • If the model cannot be measured (no served route, no credentials), ship the calibration with its research provenance and record the measurement as the named follow-up.
  • If a later measurement shows the calibration worse than baseline, revise or remove it in the same change that reports the number.

Workflow Lane

  • Current lane: Research and company ops (product-docs, source-finder, web-research, research, model-optimization, inference-serving, model-finetuning, research-brief, +20 more) - research, signals, ops, and briefings.
  • 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 a model or family is new to OMH, shipped a new generation, or changed its serving contract, and the operator wants recognition, calibration, routing, pricing, and docs checked and strengthened for it through the fixed onboarding process.

Strong routing signals: `model-optimization`, `model optimization`, `optimize for model`, `onboard new model`, `calibrate new model`, `new model calibration`, `model calibration`

Catalog Metadata

Category: optimization Phase: model-onboarding Hermes role: tracker Quality tier: evidence-gated Reasoning demand: heavy

Quality bar:

  • Probe recognition before researching: omh coding model-route --executor hermes --model <id> --effort <effort> --role implementation --json shows the family label the routing engine assigns; an unknown or generic label means the family prefix table needs a row before any calibration can attach.
  • Check calibration coverage second: the MODEL_OPTI.md coverage matrix plus both calibration tables (subagent high-effort and composer). A recognized family with no calibration is a tracked gap, not an error.
  • Research official docs first — release notes, thinking/tool-calling contract, context and output limits, pricing, speed tiers — then how other open-source harnesses handle the model. Label every finding official, official-client (the vendor's own client source), observed, or community and keep the source; on conflict the earlier label wins.
  • Author calibration as trait-to-counter: name the model's documented or observed behavior, then state the concrete counter-behavior, version-aware where generations differ. Do not restate universal protocol rules inside a family entry.
  • Distinguish speed tiers from separate models before touching routing: a speed tier is the same weights served faster and projects onto its base model; a separately trained sibling is its own chain entry. Place routing through config surfaces first (omh model-chains set, omh coding category-maestro set); shipped editorial defaults change only as a repo change with explicit owner approval; a superseded generation leaves the shipped chains, and its retired alias stays recognized and priced so a machine-level override still resolves.
  • Record cost only from documented list pricing; a model or tier without a documented price gets no entry — absence renders no estimate, never a fabricated number.
  • Close with measurement: a calibration ships measurable, and the baseline-vs-optimized benchmark pair is the named follow-up when no served route exists yet. A calibration that measures worse than baseline is revised or removed in the same change that reports the number, never kept.
Show full SKILL.md (349 more words)Show less

Handoff policy:

Keep recognition probes, research synthesis, calibration drafting, and the process checklist in Hermes. Machine-local routing placement is a config edit the operator approves; repository changes (prefix table rows, calibration text, shipped chain defaults, docs) are coding work for the selected executor lane. A drafted calibration or prepared route is prepared_not_observed, never execution or benchmark evidence.

Required inputs:

  • the model id(s) as served, and the provider or gateway serving them
  • recognition probe output for each id
  • official release/contract documentation, with community harness findings labeled separately

Expected outputs:

  • recognition and calibration coverage verdict for the family
  • trait-to-counter calibration draft (or a no-change verdict with reasons)
  • routing/pricing placement plan naming config surfaces vs repo changes
  • measurement plan naming the benchmark pair or the reason none can run yet

Artifact expectations:

  • metadata-only runtime record when a wrapper or shell is available

Safety rules:

  • Do not imply hidden Hermes runtime behavior.
  • Use the smallest verification that can prove the claim.

Runtime Evidence

Preferred harness for this skill: research.

sh
omh runtime record --skill model-optimization --harness research --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. Reply in the user's own words and the host's own voice: its SOUL.md persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done.

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.

© rlaope, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/omh-model-optimization of rlaope/oh-my-hermes.

Open the folder on GitHubat commit f772a94

Compare with similar skills

Omh Model Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Omh Model Optimization compared with similar skills
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Omh Model Optimization this skillrlaope/oh-my-hermes3.2k—~2.3kAutomated safety check: PassMIT
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Performance ReportAffitor/affiliate-skills6991 repos~2.5kAutomated safety check: PassMIT
Run Mv Hoi Reconstructionnvidia-isaac/video_to_data850—~1.5kAutomated safety check: PassCustom licence
Company Analysiszhu1090093659/dsh-trading231—~4.2kAutomated safety check: PassCustom licence

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Questions about Omh Model Optimization

What does Omh Model Optimization do?

[omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement…. Omh Model Optimization is an agent skill from rlaope/oh-my-hermes. [omh] Onboarding a newly released model generation: when a model family ships a new generation or changes its serving contract, walk the recognition, research, calibration, routing, and measurement process that keeps model handling honest and current.

When should I use Omh Model Optimization?

Omh Model Optimization fits situations like: the user says: model-optimization; model optimization; optimize for model; onboard new model.

How do I install Omh Model Optimization in Claude Code?

Run `npx skills add rlaope/oh-my-hermes --skill omh-model-optimization -a claude-code`. Or copy the skill folder (skills/omh-model-optimization in rlaope/oh-my-hermes) into .claude/skills/omh-model-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Omh Model Optimization in Codex?

Run `npx skills add rlaope/oh-my-hermes --skill omh-model-optimization -a codex`. Or copy the skill folder (skills/omh-model-optimization in rlaope/oh-my-hermes) into .agents/skills/omh-model-optimization in your project. Codex loads it when a task matches its description.

Can I use Omh Model Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rlaope/oh-my-hermes --skill omh-model-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/omh-model-optimization, .gemini/skills/omh-model-optimization, .github/skills/omh-model-optimization and .opencode/skills/omh-model-optimization in your project.

What does Omh Model Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Omh Model Optimization is instructions for the agent only.

Does Omh Model Optimization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Omh Model Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Omh Model Optimization use?

Omh Model Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Omh Model Optimization use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Omh Model Optimization?

Skills that share tags, products or a category with Omh Model Optimization: Wp Performance Review (elvismdev/claude-wordpress-skills, 235 stars), Align Human (agentscope-ai/OpenJudge, 868 stars), Performance Report (Affitor/affiliate-skills, 699 stars) and Run Mv Hoi Reconstruction (nvidia-isaac/video_to_data, 850 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Omh Model Optimization?

rlaope (a GitHub user) maintains it in rlaope/oh-my-hermes, which has 3,207 GitHub stars. The repository holds 143 skills in this directory. The repository was last updated on October 8, 2026.

Source: rlaope/oh-my-hermes on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.