Package Optimizer

Community

Mathews-Tom

package-optimizer

Evaluate one existing package or bounded package family from recorded evaluator evidence and a capability profile, then propose retain, simplify, strengthen, retire, or inconclusive without editing. Use when optimizing a skill, agent, hook, rule, command, utility, or preset; evaluating whether package detail is justified; reviewing a model-refresh impact; or preparing an approval-gated package improvement. Refuses missing, stale, or incomparable evidence and never edits without explicit approval. Not for scoring static package conformance or rubric dimensions; use package-evaluator.

Overview

Publisher	Mathews-Tom
Repository	`armory`
Skill name	`package-optimizer`
Stars	318
Forks	47
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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 Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Package Optimizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/package-optimizer .claude/skills/package-optimizer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Package Optimizer 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 Package Optimizer 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 Package Optimizer 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.

Package Optimizer

Turn existing package evidence into one bounded, reviewable proposal. The optimizer is proposal-only: it never edits a package, changes a profile, or starts a benchmark run.

Scope

Situation	Action
One package has current evaluator evidence	Propose one disposition.
A bounded family shares the same evidence/profile	Propose one family plan with a per-package row.
Evidence is missing, stale, structurally invalid, or from another profile	Return `inconclusive`; name the unresolved package decision and request the smallest recertification needed.
User asks to apply a proposal	Require explicit approval, preserve before artifacts, then hand the edit to the normal package workflow.
User asks for a broad benchmark before identifying a package decision	Refuse the expansion; identify the package decision first.

Required inputs

Package scope — one type/name package or an explicitly bounded family.
Current evidence — static conformance and relevant eval-case result or a recorded behavioral result. Cite exact commands, artifacts, or case IDs.
Capability profile — model/client/tool-surface identity when behavioral evidence depends on one. Structural-only evidence is labelled as such.
Decision question — what behavior should be retained, simplified, or strengthened.

Do not infer effectiveness from package length, heading count, or static score.

Evidence validity

Reject evidence when any condition holds:

package path, version, or evaluator case differs from the proposed scope;
a behavioral result has no model/client/tool-surface identity;
a profile is older than a recorded package change or cannot be compared to the requested profile;
the evidence omits a failure, safety outcome, or relevant evaluator result;
an M4 recertification report pools model targets or lacks complete cells.

A rejection uses the single output format below with Disposition: inconclusive, Proposed change: none, and Approval required: yes. Its Unresolved package decision must name the package scope and decision question. A recertification request repeats that exact field; do not request a benchmark without it.

Procedure

Resolve the package scope and read its definition, evaluator cases, and current conformance output.
State the decision question and evidence class: structural, behavioral, or profile-scoped behavioral.
Verify evidence validity. Stop with inconclusive on any invalid input.
Select exactly one disposition:
- retain — evidence supports the current contract.
- simplify — evidence identifies redundant detail and the retained contract/evaluator proves the smaller scope.
- strengthen — a documented failure requires a concrete contract addition.
- retire — explicit deprecation or replacement evidence supports removal.
- inconclusive — evidence cannot support a safe change.
Produce a proposal. For non-retain dispositions, name exact sections or files to change and the evaluator behavior that must remain true.
Stop. Do not edit. Require the user to explicitly approve the proposal.

Approval handoff

An approval must name the package, disposition, and proposal ID. The downstream applying workflow—not this skill—must:

Save the original package artifact and evaluator evidence.
Apply only the approved package/family change.
Run static conformance and relevant eval cases.
Record before/after artifacts, verification output, profile identity, and any regression.
Revert the bounded package change on regression. Do not broaden the scope.

Output format

text
## Package Optimization Proposal

Scope: <type/name or bounded family>
Decision question: <question>
Evidence class: <structural | behavioral | profile-scoped behavioral>
Evidence:
- <command/artifact/case and observed result>
Capability profile: <identity | not applicable>

Disposition: <retain | simplify | strengthen | retire | inconclusive>
Rationale: <evidence-backed explanation>
Unresolved package decision: <scope + decision question | not applicable>

Proposal ID: <stable scope + evidence identifier>
Proposed change: <none | exact files/sections and intended behavior>
Preservation check: <existing evaluator/eval case>
Approval required: yes

Smallest next action: <concrete action>

Guardrails

Never edit without explicit approval.
Never optimize all packages at once.
Never turn missing evidence into a simplification recommendation.
Never claim token, cost, or quality improvement without recorded evidence.
Prefer retain or inconclusive over speculative change.

Bundled files

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

evals/cases.yaml3 KB

Frequently asked questions

What does the Package Optimizer AI skill do?

Why use Package Optimizer on TypingMind?

Because you install it once and use it with any model. Package Optimizer 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 Package Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/package-optimizer. 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 Package Optimizer?

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 Package Optimizer?

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

Is the Package Optimizer AI skill free?

Yes. It is published on GitHub by Mathews-Tom 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Package Optimizer

Scope

Required inputs

Evidence validity

Procedure

Approval handoff

Output format

Guardrails

Bundled files

Frequently asked questions

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