Agent skill

Create Optimization Hypothesis

by ai-dynamo in ai-dynamo/dynamo

Materializes one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready deploy-draft.yaml by applying only its selected DGD change to the current successful manifest.

Apache-2.0Auto-check passedDevOps & Cloud

Install Create Optimization Hypothesis

skills CLI
$ npx skills add ai-dynamo/dynamo --skill create-optimization-hypothesis -a claude-code

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

GitHub CLI
$ gh skill install ai-dynamo/dynamo create-optimization-hypothesis --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/ai-dynamo/dynamo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/create-optimization-hypothesis .claude/skills/create-optimization-hypothesis && 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
create-optimization-hypothesis
GitHub stars
8.2k
Token cost
~1.8k tokens
SKILL.md length
855 words
Files
1
Skills in repo
27
Repo updated
First seen
Licence
Apache-2.0

At a glance

Materializes one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready deploy-draft.yaml by applying only its selected DGD change to the current successful manifest.

  • Works in 5 steps: Locate the selected knob in… → Resolve its exact YAML path and current… → If it is embedded in a command or… → …
  • Tasks that involve Deployment
  • SKILL.md covers Inputs, Read The Consultation, Resolve The Manifest Change and Materialize The Draft, plus 3 more sections
  • Calls kubectl

What it does

Create Optimization Hypothesis is an agent skill from ai-dynamo/dynamo. Materializes one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready deploy-draft.yaml by applying only its selected DGD change to the current successful manifest. Use after consult-perf-knowledge writes a proposed consultation in the current deployment iteration's next-candidate directory.

Its SKILL.md is about 1.8k 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 DevOps & Cloud, covering Deployment. The repository describes itself as: A Datacenter Scale Distributed Inference Serving Framework. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Deployment

Example prompts

  • “Use the create-optimization-hypothesis skill to materializ one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready…”
  • “/create-optimization-hypothesis”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Locate the selected knob in DEPLOY_ROOT/applied_manifests/deploy.yaml.
  2. Resolve its exact YAML path and current value from that manifest.
  3. If it is embedded in a command or argument string, identify the owning YAML path and change only the selected
  4. If the consultation explicitly requires adding an absent setting, add only that setting in the location used by
  5. For a coupled bundle, resolve every explicitly named setting and no others.

What it can do on your machine

Read from SKILL.md and the folder at commit 5e82beb. 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

    Shell commands in SKILL.md call:

    • kubectl

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

  • Network

    No URLs in SKILL.md. Its commands use kubectl, which can reach the network depending on how they are called.

    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

Create Optimization Hypothesis loads about 1.8k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 855 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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 ai-dynamo/dynamo at commit 5e82beb, republished under its Apache-2.0 licence (© ai-dynamo). 855 words, ~1,783 tokens.

Download SKILL.mdSave it as .claude/skills/create-optimization-hypothesis/SKILL.md (or your agent's skills folder).
name
create-optimization-hypothesis
description
Materializes one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready deploy-draft.yaml by applying only its selected DGD change to the current successful manifest. Use after consult-perf-knowledge writes a proposed consultation in the current deployment iteration's next-candidate directory.
license
Apache-2.0
metadata.author
NVIDIA
metadata.tags
dynamo, optimization, hypothesis, kubernetes, yaml

Create Optimization Hypothesis

<!--
SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: Apache-2.0
-->

Materialize an already-reasoned proposal. Treat knowledge-consult.md as a flexible reasoning record, not a rigid schema. Do not select a different lever, broaden the proposal, deploy, benchmark, or approve it.

Inputs

Require:

  • the current DEPLOY_ROOT;
  • DEPLOY_ROOT/applied_manifests/deploy.yaml; and
  • DEPLOY_ROOT/next-candidate/knowledge-consult.md written by consult-perf-knowledge.

Use:

text
<EXP_ROOT>/artifacts/deploy-iter-<NNN>/next-candidate/

as HYPOTHESIS_ROOT. The enclosing deploy-iter-<NNN> remains the analyzed source iteration. Do not create the next deployment-iteration directory; recipe-deployer owns it after challenger approval.

Read The Consultation

Read the entire consultation. Keep its freedom of form: do not require a particular bullet order, table shape, or subsection beyond the core Decision, Evidence, Proposed Change, and Materialization Handoff sections.

Inspect Decision first:

  • For Status: no-proposal or Status: blocked, stop without creating deploy-draft.yaml.
  • For Status: proposed, continue only when the remaining content makes one candidate actionable.
  • For a missing, conflicting, or unknown status, return the consultation without creating a draft.

For a proposed candidate, require the consultation to communicate, anywhere in its relevant sections:

  • the successful source manifest path and SHA256;
  • at least three distinct qualifying evidence categories, including AIPerf profiler data;
  • the selected primary knob, its owner, and an exact target setting or state;
  • whether the candidate is single-knob or coupled-bundle;
  • one intended mechanism connecting the change to the expected measurable effect;
  • the performance question and target operating region for evaluating the candidate;
  • the important risks or metrics that may regress; and
  • for a coupled bundle, every required setting, the qualifying coupling reason, and any required follow-up ablation.

Accept evidence and reasoning as concise prose or tables. Do not require the consultation to precompute YAML paths, current values, a source-to-draft diff, a separate validation plan, or a materialization-status field. Do not repeat the performance analysis or re-rank the selected lever. Count the evidence categories represented by the entries; do not rely only on the declared category count.

If the proposed target value, affected component, bundle membership, or intended mechanism is ambiguous, return the consultation to consult-perf-knowledge. Do not choose a value, add a related optimization, or infer a broader candidate.

Resolve The Manifest Change

Require the recorded source path to identify the current successful manifest. Recompute its SHA256 and require it to match Source manifest SHA256 in Materialization Handoff. Then translate the selected proposal into the smallest mechanical manifest edit:

  1. Locate the selected knob in DEPLOY_ROOT/applied_manifests/deploy.yaml.
  2. Resolve its exact YAML path and current value from that manifest.
  3. If it is embedded in a command or argument string, identify the owning YAML path and change only the selected fragment.
  4. If the consultation explicitly requires adding an absent setting, add only that setting in the location used by the owning component.
  5. For a coupled bundle, resolve every explicitly named setting and no others.

Use cited source or official documentation only to confirm the selected knob's syntax and placement. Do not use it to choose a different knob or target value. If the selected knob maps to multiple components or the requested state cannot be mapped unambiguously, stop and return the consultation.

Show full SKILL.md (359 more words)Show less

Materialize The Draft

Create ${HYPOTHESIS_ROOT}/deploy-draft.yaml from the exact successful source manifest:

  1. Preserve the source formatting and key order where practical.
  2. Apply only the resolved change selected in knowledge-consult.md.
  3. For a flag embedded in a command or argument string, replace only the selected fragment.
  4. Preserve API version, kind, DGD name, model identity, images, secret references, and benchmark wiring.
  5. Do not edit the tracked recipe, current applied manifest, benchmark files, or prior iteration artifacts.
  6. Do not add optional tuning to a functionality-required bundle.
  7. Remove incidental formatting or ordering changes from the source-to-draft diff.

Render and validate in a temporary file before replacing the final draft. If an existing deploy-draft.yaml already has the same source hash and resolved semantic diff, validate and reuse it. If it differs, do not overwrite it; report the conflict for review.

Validate The Draft

Before finalizing:

  • parse the draft as YAML;
  • require the source and draft to remain DynamoGraphDeployment resources;
  • preserve the source API version, kind, and metadata.name;
  • compute a source-to-draft semantic diff;
  • require the semantic diff to contain exactly the resolved selected change;
  • require all fields in an allowed coupled bundle and no others;
  • confirm one independently testable mechanism under agent-docs/rules/optimization/one-variable.md;
  • preserve target-fixed model, framework, precision, hardware, workload, and SLO constraints;
  • verify the draft contains no secret values; and
  • compute SHA256 hashes for the source and draft.

Perform local validation only. Do not mutate Kubernetes, run kubectl apply, use server-side dry run, launch a smoke test, or run AIPerf.

Complete The Handoff Record

After the final draft passes validation, update only the Materialization Handoff section of knowledge-consult.md:

  • replace Draft manifest SHA256: pending with the final draft SHA256;
  • add Materialization result: created;
  • add the local YAML and semantic-diff validation result; and
  • add a compact table containing every exact YAML path and its verified before and after values.

Preserve the consultation's free-form reasoning and all evidence. Do not rewrite its Decision, Reasoning, Evidence, or Proposed Change sections. Do not write separate hypothesis-ledger.md or hypothesis-ledger.json files; knowledge-consult.md is the single reasoning and handoff record.

Return

Return these two files to hypothesis-challenger:

  • ${HYPOTHESIS_ROOT}/knowledge-consult.md;
  • ${HYPOTHESIS_ROOT}/deploy-draft.yaml.

The draft is a proposal, not authorization to deploy.

© ai-dynamo, Apache-2.0. 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 .agents/skills/create-optimization-hypothesis of ai-dynamo/dynamo.

Open the folder on GitHubat commit 5e82beb

Compare with similar skills

Create Optimization Hypothesis 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.

Create Optimization Hypothesis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Create Optimization Hypothesis this skillai-dynamo/dynamo8.2k—~1.8kAutomated safety check: PassApache-2.0
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb6.7k—~4kAutomated safety check: NotesApache-2.0
KubeSphere ServiceMesh Managerkubesphere/kubesphere17k—~2.4kAutomated safety check: PassCustom licence
Vercelremotion-dev/remotion62k—~1.2kAutomated safety check: PassCustom licence
AWS Cdk Developmentzxkane/aws-skills3672 repos~2.5kAutomated safety check: PassMIT

Similar skills

  • Kubeshark Installer

    kubeshark/kubeshark

    Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.

    12k GitHub stars~3.6k tokensUpdated 6 days ago
    DevOps & CloudAuto-check: notes
  • GreptimeDB Dev Docker Image

    GreptimeTeam/greptimedb

    Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.

    6.7k GitHub stars~4k tokensUpdated 2 days ago
    DevOps & CloudAuto-check: notes
  • KubeSphere ServiceMesh Manager

    kubesphere/kubesphere

    Installs, checks and troubleshoots the KubeSphere ServiceMesh extension (Istio, Kiali, Jaeger), including grayscale release, sidecar injection, topology and tracing issues.

    17k GitHub stars~2.4k tokensUpdated 2 mo ago
    DevOps & CloudAuto-check passed
  • Vercel

    remotion-dev/remotion

    Official

    Set up a Codex monitor for Vercel deployments and preview URLs.

    62k GitHub stars~1.2k tokensUpdated today
    DevOps & CloudAuto-check passed
  • AWS Cdk Development

    zxkane/aws-skills

    AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.

    367 GitHub starsUsed in 2 repos~2.5k tokens
    DevOps & CloudAuto-check passed
  • Senior DevOps Toolkit

    maslennikov-ig/claude-code-orchestrator-kit

    Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup…

    259 GitHub starsUsed in 6 repos~1.1k tokens
    DevOps & CloudAuto-check: notes

More from ai-dynamo/dynamo

All 27 skills in this repo
  • Visual Review

    ai-dynamo/dynamo

    Create self-contained interactive HTML code-review dashboards from GitHub or GitLab pull requests, checked-out branch diffs, or supplied unified diffs, with correctness and safe-to-merge scores…

    8.2k GitHub stars~4.5k tokensUpdated today
    Auto-check passed
  • Fern Components

    ai-dynamo/dynamo

    Knowledge of Fern's built-in MDX component library (accordions, callouts, cards, steps, tabs, code blocks, API-reference snippets, and more) for authoring docs pages.

    8.2k GitHub stars~2.8k tokensUpdated today
    Auto-check passed
  • Fern Navigation

    ai-dynamo/dynamo

    Knowledge of Fern's site-level navigation and structure configuration — how a docs site is organized in docs.yml (and product/version .yml files) using sections, pages, folders, tabs, tab variants…

    8.2k GitHub stars~2.2k tokensUpdated today
    Auto-check passed
  • Dynamo Agent Harness

    ai-dynamo/dynamo

    Drives persistent Claude Code, Codex, or OpenCode agent sessions through a Dynamo OpenAI/Anthropic-compatible endpoint over Agent Client Protocol (ACP).

    8.2k GitHub stars~1.2k tokensUpdated today
    Auto-check passed
  • Benchmark and profile the Dynamo frontend (dynamo.frontend HTTP + tokenizer + KV router) against mock workers (dynamo.mocker).

    8.2k GitHub stars~3.5k tokensUpdated today
    Auto-check: notes
  • Selects and freezes a question-driven AIPerf workload, objective, load policy, and Kubernetes execution manifest for a successfully deployed Dynamo candidate.

    8.2k GitHub stars~1.7k tokensUpdated today
    Auto-check passed

Categories

Questions about Create Optimization Hypothesis

What does Create Optimization Hypothesis do?

Materializes one evidence-backed proposal from a flexible knowledge-consult.md into a challenger-ready deploy-draft.yaml by applying only its selected DGD change to the current successful manifest. Create Optimization Hypothesis is an agent skill from ai-dynamo/dynamo.yaml by applying only its selected DGD change to the current successful manifest.

When should I use Create Optimization Hypothesis?

Create Optimization Hypothesis fits situations like: tasks that involve Deployment.

How do I install Create Optimization Hypothesis in Claude Code?

Run `npx skills add ai-dynamo/dynamo --skill create-optimization-hypothesis -a claude-code`. Or copy the skill folder (.agents/skills/create-optimization-hypothesis in ai-dynamo/dynamo) into .claude/skills/create-optimization-hypothesis in your project. Claude Code loads it when a task matches its description.

How do I install Create Optimization Hypothesis in Codex?

Run `npx skills add ai-dynamo/dynamo --skill create-optimization-hypothesis -a codex`. Or copy the skill folder (.agents/skills/create-optimization-hypothesis in ai-dynamo/dynamo) into .agents/skills/create-optimization-hypothesis in your project. Codex loads it when a task matches its description.

Can I use Create Optimization Hypothesis 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 ai-dynamo/dynamo --skill create-optimization-hypothesis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-optimization-hypothesis, .gemini/skills/create-optimization-hypothesis, .github/skills/create-optimization-hypothesis and .opencode/skills/create-optimization-hypothesis in your project.

What does Create Optimization Hypothesis need to run?

Going by SKILL.md and its folder, Create Optimization Hypothesis needs the command-line tools its instructions call (kubectl).

Does Create Optimization Hypothesis 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 Create Optimization Hypothesis 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 Create Optimization Hypothesis use?

Create Optimization Hypothesis is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Optimization Hypothesis use?

About 1.8k tokens (SKILL.md is roughly 7.1k 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 Create Optimization Hypothesis?

Skills that share tags, products or a category with Create Optimization Hypothesis: Kubeshark Installer (kubeshark/kubeshark, 12k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars) and Vercel (remotion-dev/remotion, 62k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Optimization Hypothesis?

ai-dynamo (a GitHub organization) maintains it in ai-dynamo/dynamo, which has 8,238 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on October 7, 2026.

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