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optimization

optimization is a skill for Claude Code from ChrisGVE/localdata-mcp. It costs 22 tokens per session (507 once invoked), scanned A, original, Apache-2.0.

A workflow for finding the best allocation of resources, schedule, cost, or process settings while respecting limits and requirements.

In plain words
What is it for?
Use it for resource allocation, staff or production scheduling, cost reduction, throughput improvement, and process tuning.
Why use it?
It helps turn a practical planning problem into a clear mathematical model with an objective and constraints. It also checks whether the source data contains the values needed for a reliable solution.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the localdata-mcp plugin — 18 skills, 11 agents, 1 MCP server shipped together

not rated 4repo +1 23d ago A scan 22 tokens original Apache-2.0

Good fit Use it for resource allocation, staff or production scheduling, cost reduction, throughput improvement, and process tuning.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chrisgve/localdata-mcp/optimization
View source ↗ ChrisGVE/localdata-mcp
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ChrisGVE/localdata-mcp --skill optimization
Clone the repo
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/optimization.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/optimization)
Your own site
<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/optimization"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/optimization.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 507 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.00507
Opus 5 $0.00011 $0.00253
Sonnet 5 $0.00004 $0.00101
Haiku 4.5 $0.00002 $0.00051

Measured 7d ago against content hash 0e150fd9f6ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/modeling/optimization/SKILL.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Optimization

Formulate and solve optimization problems from data — resource allocation, scheduling, cost minimization, or process tuning.

Steps

  1. Understand the objective. From the user's question, identify what is being optimized (minimize cost, maximize throughput, best allocation) and what the constraints are (budget limits, capacity, time windows, quality thresholds).

  2. Extract problem data. Call describe_database with the database name from $ARGUMENTS. Identify tables containing:

    • Decision variables (what can be changed)
    • Objective coefficients (costs, profits, rates)
    • Constraint parameters (capacities, limits, requirements)

  3. Profile the data. Call execute_query to pull the relevant values. Verify completeness with get_data_quality_report. Missing constraint data makes optimization unreliable.

  4. Formulate the problem. Translate the data into an optimization formulation:

    • Objective function (linear or nonlinear)
    • Decision variables and their bounds
    • Constraints (equality and inequality)
    • Report the formulation clearly before solving

  5. Solve. Apply the appropriate optimization approach (available when optimization domain tools are exposed):

    • Linear programming for linear objectives and constraints
    • Constrained optimization for nonlinear problems
    • Assignment problems for matching tasks to resources
    • Network optimization for flow and routing

  6. Analyze the solution. Examine:

    • Optimal objective value
    • Decision variable values at the optimum
    • Which constraints are binding (at their limit) vs. slack
    • Sensitivity: how much would the objective change if a constraint were relaxed?

  7. Validate against reality. Call execute_query to compare the optimal solution against historical performance. Is the improvement realistic? Are there practical constraints the model does not capture?

  8. Present results. Provide:

    • Problem formulation summary
    • Optimal solution with all variable values
    • Objective value and improvement over baseline
    • Binding constraints and sensitivity analysis
    • Implementation recommendations and caveats

Read the full file on GitHub · 49 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 49 lines · 22 tokens per session scan A 0e150fd9f6ca

Subscribe to this mod's changes

optimization is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 23d ago), licensed Apache-2.0. It adds 22 tokens to every session and 507 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsClaude CodePlanningai-toolsclaude-desktopdata-analyticsfastmcpmcp
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