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Omh Context Budget Review

CommunityPopular
rlaope
omh-context-budget-review

[omh] Hermes Context Budget Review workflow: plan compact context, token/cost budgets, summarization checkpoints, and overflow recovery before long agent work. Use when the user says: context-budget-review, context budget review, context budget, token budget review, token budget, prompt budget, prompt caching, prompt cache.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-context-budget-review
Stars
2.7K
Forks
194
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Context Budget Review 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-context-budget-review .claude/skills/omh-context-budget-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Context Budget Review 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 Context Budget Review 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 Context Budget Review 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.

Context Budget Review

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

Why This Exists

context-budget-review ports ECC's context-budget and token-budget instincts into OMH as a compactness gate that protects long-running work without redefining success around a smaller task.

Do Not Use When

  • The user asks for live token/cost telemetry; use ops-observability-card.
  • The user asks to continue a loopable goal; use loop unless budget planning is the explicit blocker.
  • The task is a short one-step answer with no meaningful context risk.

Examples

Good example:

  • Prompt: context-budget-review 이 장기 PR 작업에서 어떤 맥락을 꼭 유지하고 언제 요약해야 하는지 잡아줘.
  • Expected behavior: Prepare context_budget_plan/v1, must_keep_context_pack/v1, checkpoint plan, risk register, and overflow recovery route.
  • Why: The request is about preserving context quality during long-running agent work.

Bad example:

  • Prompt: context-budget-review 토큰 아끼려고 원래 목표를 더 작은 목표로 바꿔줘.
  • Expected behavior: Reject goal shrinking and instead compact context while preserving the full objective and evidence gaps.
  • Why: Budget review optimizes context handling, not the user's requested end state.

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 before long-running research, coding, review, or multi-agent work when context, token, cost, or summary drift could break quality.

Strong routing signals: `context-budget-review`, `context budget review`, `context budget`, `token budget review`, `token budget`, `prompt budget`, `prompt caching`, `prompt cache`, `cache hygiene`, `context compaction`, `compact context`, `too much context`, `context window`, `running out of context`, `hand off to a new session`, `summarization checkpoint`, `budget this task`, `컨텍스트 예산`, `토큰 예산`, `컨텍스트 압축`, `요약 체크포인트`

Catalog Metadata

Category: observability Phase: context-budget-review Quality tier: context-budget-gated Reasoning demand: standard

Quality bar:

  • Name must-keep context before summarizing or delegating long work.
  • Separate durable requirements, volatile status, file refs, verification evidence, and open blockers.
  • Define checkpoint cadence, overflow recovery, and continuity verification.
  • Use bounded copy while preserving the full objective and evidence gaps.
  • Count the must-keep pack by item class, so a replaced pack reports which class lost entries instead of only that a digest moved; a pack that recorded no classes reports the comparison unavailable and never reports zero items.
  • Keep prompt-prefix placement cache-stable: fixed section order, volatile bytes never above the fold, mid-run changes as appended messages never system-prompt mutations — load references/cache-placement.md for the placement rules.

Required inputs:

  • task or workflow scope
  • expected duration, artifacts, and handoff surfaces
  • available context sources and must-keep facts
  • token, cost, latency, or message-size constraints when known

Expected outputs:

  • context_budget_plan/v1
  • must_keep_context_pack/v1
  • must_keep_item_class_delta/v1 when a pack replaces an earlier one
  • summarization_checkpoint_plan/v1
  • budget_risk_register/v1
  • overflow_recovery_route/v1
  • not-evidence boundary

Artifact expectations:

  • context_budget_plan/v1 with scope, max visible context, source priority, discard rules, and checkpoint cadence
  • must_keep_context_pack/v1 with durable facts, file refs, decisions, PR/CI state, and blocked assumptions
  • must_keep_item_class_delta/v1 naming the item class that lost entries rather than reporting a digest difference, over the closed vocabulary prohibitions, decisions, open_questions, requirements, paths, pr_state, verification_gaps
  • summarization_checkpoint_plan/v1 with when to compact, what to preserve, and how to verify continuity
  • budget_risk_register/v1 separating estimated cost/token/latency risk from provider-observed truth

Safety rules:

  • Do not claim provider billing, exact token counts, or runtime compaction occurred without observed evidence.
  • Do not drop user requirements, file paths, PR state, verification gaps, or explicit constraints during compaction.
  • Keep estimated budget risk, observed usage, checkpoint summaries, and completion evidence separate.
  • Do not use budget pressure as a reason to shrink the user's requested end state.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Omh Context Budget Review AI skill do?

[omh] Hermes Context Budget Review workflow: plan compact context, token/cost budgets, summarization checkpoints, and overflow recovery before long agent work. Use when the user says: context-budget-review, context budget review, context budget, token budget review, token budget, prompt budget, prompt caching, prompt cache.

Why use Omh Context Budget Review on TypingMind?

Because you install it once and use it with any model. Omh Context Budget Review 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 Context Budget Review in TypingMind?

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

Which AI models can use Omh Context Budget Review?

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 Context Budget Review?

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

Is the Omh Context Budget Review 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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