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Agent skill

Optimization Objective Map

by SilvioBaratto in SilvioBaratto/optimizer

Map a chosen ConstraintSet.objective to the correct optimizer cone / risk-measure and factory config.

Custom licenceAuto-check passed

Install Optimization Objective Map

skills CLI
$ npx skills add SilvioBaratto/optimizer --skill optimization-objective-map -a claude-code

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

GitHub CLI
$ gh skill install SilvioBaratto/optimizer optimization-objective-map --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/SilvioBaratto/optimizer.git skills-src && mkdir -p .claude/skills && cp -r skills-src/fund/src/fund/skills/optimization-objective-map .claude/skills/optimization-objective-map && 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
optimization-objective-map
GitHub stars
176
Token cost
~510 tokens
SKILL.md length
212 words
Files
3
Skills in repo
10
Repo updated
First seen
Licence
Custom licence

At a glance

Map a chosen ConstraintSet.objective to the correct optimizer cone / risk-measure and factory config.

  • Works in 4 steps: Read the objective + risk measure +… → Map objective → cone / risk measure… → Apply the cardinality cap + min-weight,… → …
  • The allocator must turn an objective (min-risk
  • SKILL.md covers When to use, Load-bearing rule, Procedure and Boundaries, plus 1 more section
  • Runs Python scripts from its folder

What it does

Optimization Objective Map is an agent skill from SilvioBaratto/optimizer. Map a chosen ConstraintSet.objective to the correct optimizer cone / risk-measure and factory config. Use when the allocator must turn an objective (min-risk, max-utility, max-ratio, risk-budget) into a concrete optimizer configuration before optimizeportfolio. Does NOT emit weights — the optimizer computes the weights.

Its SKILL.md is about 510 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `factories.py` and `reference.md`).

The repository describes itself as: Quantitative portfolio construction and optimization platform built on skfolio and scikit-learn.

When your agent uses it

  • The allocator must turn an objective (min-risk
  • Risk-budget) into a concrete optimizer configuration before optimizeportfolio

Example prompts

  • “/optimization-objective-map”

Requirements

  • Python 3

Workflow steps

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

  1. Read the objective + risk measure + bounds from the ConstraintSet.
  2. Map objective → cone / risk measure (table in reference.md; call skeletons in
  3. Apply the cardinality cap + min-weight, a robust uncertainty set if the profile is
  4. Call optimize_portfolio(returns_asof, universe, constraints); hand weights +

What it can do on your machine

Read from SKILL.md and the folder at commit 63e897b. 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

    Ships script files (Python), which the agent can run.

    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

Optimization Objective Map loads about 510 tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 212 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
When it runs · the whole SKILL.md, loaded when a task matches
~510

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

Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 212 words (~510 tokens).

“The allocator's core step: pick the optimizer FAMILY and risk measure that match the profile's objective, emit the factory config, and let the optimizer solve.”

— opening of SKILL.md by SilvioBaratto, Custom licence
name
optimization-objective-map

Read the full SKILL.md on GitHub

Files

SKILL.md and 2 other files in fund/src/fund/skills/optimization-objective-map of SilvioBaratto/optimizer.

  • SKILL.md
  • factories.py
  • reference.md

Open the folder on GitHubat commit 63e897b

Compare with similar skills

Optimization Objective Map 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.

Optimization Objective Map compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Optimization Objective Map this skillSilvioBaratto/optimizer176—~510Automated safety check: PassCustom licence
SQL Optimizationgithub/awesome-copilot40k2 repos~2.3kAutomated safety check: PassMIT
Map Optimization Strategybenchflow-ai/skillsbench1.8k—~1.1kAutomated safety check: PassApache-2.0
Agent Performance Optimizerruvnet/ruflo74k2 repos~3.6kAutomated safety check: PassMIT
Database Optimizerdavila7/claude-code-templates32k8 repos~2.5kAutomated safety check: PassMIT
Prompt Optimizeraffaan-m/ECC275k2 repos~2.4kAutomated safety check: PassMIT

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  • Risk Limits Check

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Questions about Optimization Objective Map

What does Optimization Objective Map do?

Map a chosen ConstraintSet.objective to the correct optimizer cone / risk-measure and factory config. Optimization Objective Map is an agent skill from SilvioBaratto/optimizer.objective to the correct optimizer cone / risk-measure and factory config.

When should I use Optimization Objective Map?

Optimization Objective Map fits situations like: the allocator must turn an objective (min-risk; risk-budget) into a concrete optimizer configuration before optimizeportfolio.

How do I install Optimization Objective Map in Claude Code?

Run `npx skills add SilvioBaratto/optimizer --skill optimization-objective-map -a claude-code`. Or copy the skill folder (fund/src/fund/skills/optimization-objective-map in SilvioBaratto/optimizer) into .claude/skills/optimization-objective-map in your project. Claude Code loads it when a task matches its description.

How do I install Optimization Objective Map in Codex?

Run `npx skills add SilvioBaratto/optimizer --skill optimization-objective-map -a codex`. Or copy the skill folder (fund/src/fund/skills/optimization-objective-map in SilvioBaratto/optimizer) into .agents/skills/optimization-objective-map in your project. Codex loads it when a task matches its description.

Can I use Optimization Objective Map 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 SilvioBaratto/optimizer --skill optimization-objective-map -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/optimization-objective-map, .gemini/skills/optimization-objective-map, .github/skills/optimization-objective-map and .opencode/skills/optimization-objective-map in your project.

What does Optimization Objective Map need to run?

Going by SKILL.md and its folder, Optimization Objective Map needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Optimization Objective Map 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 Optimization Objective Map 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 Optimization Objective Map use?

Optimization Objective Map has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.

How many tokens does Optimization Objective Map use?

About 510 tokens (SKILL.md is roughly 2k 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 Optimization Objective Map?

Skills that share tags, products or a category with Optimization Objective Map: SQL Optimization (github/awesome-copilot, 40k stars), Map Optimization Strategy (benchflow-ai/skillsbench, 1.8k stars), Agent Performance Optimizer (ruvnet/ruflo, 74k stars) and Database Optimizer (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Optimization Objective Map?

SilvioBaratto (a GitHub user) maintains it in SilvioBaratto/optimizer, which has 176 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 7, 2026.

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