Agent skill

Algo Sc Routing

by asgard-ai-platform in asgard-ai-platform/skills

Solve vehicle routing problems to optimize delivery routes under capacity and time constraints.

MITAuto-check passed

Install Algo Sc Routing

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a claude-code

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

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-sc-routing .claude/skills/algo-sc-routing && 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/

Paste into Claude Code

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .claude/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-sc-routing .agents/skills/algo-sc-routing && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .agents/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-sc-routing .cursor/skills/algo-sc-routing && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .cursor/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-sc-routing

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-sc-routing .gemini/skills/algo-sc-routing && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .gemini/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-sc-routing .github/skills/algo-sc-routing && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .github/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-sc-routing --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-sc-routing .opencode/skills/algo-sc-routing && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "algo-sc-routing" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-sc-routing into .opencode/skills/algo-sc-routing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-sc-routing", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: algo-sc-routing
GitHub stars: 242
Token cost: ~1.1k tokens
SKILL.md length: 414 words
Files: 4 (incl. references)
Skills in repo: 207
Repo updated: Jun 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Solve vehicle routing problems to optimize delivery routes under capacity and time constraints.

Works in 4 steps: Input Validation → Core Algorithm → Verification → …
The user needs to plan delivery routes
SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Sc Routing is an agent skill from asgard-ai-platform/skills. Solve vehicle routing problems to optimize delivery routes under capacity and time constraints. Use this skill when the user needs to plan delivery routes, minimize transportation costs, or optimize fleet utilization — even if they say 'delivery route optimization', 'fleet routing', or 'minimize driving distance'.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/clarke-wright.md` and `references/metaheuristics.md`).

The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

The user needs to plan delivery routes
Minimize transportation costs
Optimize fleet utilization — even if they say delivery route optimization
Minimize driving distance

Example prompts

“delivery route optimization”
“fleet routing”
“minimize driving distance”
“/algo-sc-routing”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Input Validation
Core Algorithm
Verification
Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
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

Algo Sc Routing loads about 1.1k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 414 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~83
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~7.5k

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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 414 words, ~1,100 tokens.

Download SKILL.mdSave it as .claude/skills/algo-sc-routing/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: algo-sc-routing
description: Solve vehicle routing problems to optimize delivery routes under capacity and time constraints. Use this skill when the user needs to plan delivery routes, minimize transportation costs, or optimize fleet utilization — even if they say 'delivery route optimization', 'fleet routing', or 'minimize driving distance'.
metadata.category: WP-41 供應鏈演算法
metadata.tags: supply-chain, vrp, routing, logistics

Vehicle Routing Problem (VRP)

Overview

VRP determines optimal routes for a fleet of vehicles to serve a set of customers from a depot, minimizing total distance or cost. NP-hard — exact solutions only feasible for small instances (< 25 nodes). Practical solutions use heuristics (Clarke-Wright savings, sweep) or metaheuristics (simulated annealing, genetic algorithm).

When to Use

Trigger conditions:

Planning daily delivery routes for a fleet of vehicles
Minimizing total travel distance/time under capacity constraints
Optimizing route assignments across multiple vehicles

When NOT to use:

For single-vehicle route optimization (use TSP solvers)
For real-time dynamic routing with continuous order arrivals (use online algorithms)

Algorithm

IRON LAW: VRP Is NP-Hard — Exact Solutions Don't Scale
For n customers, the solution space grows factorially. Exact methods
(branch and bound) work for n < 25. For real-world problems (50-1000+
customers), heuristics are REQUIRED. A good heuristic solution within
5% of optimal is far more valuable than an optimal solution that takes
hours to compute.

Phase 1: Input Validation

Collect: depot location, customer locations and demands, vehicle capacity, number of vehicles, time windows (if applicable), distance/time matrix. Gate: All locations geocoded, demand doesn't exceed vehicle capacity per customer.

Phase 2: Core Algorithm

Clarke-Wright Savings Heuristic:

Start with each customer on its own route (depot → customer → depot)
Compute savings for merging route pairs: s(i,j) = d(depot,i) + d(depot,j) - d(i,j)
Sort savings descending
Merge routes greedily if capacity constraint allows
Improve with 2-opt (swap edges within routes) and or-opt (move customers between routes)

Phase 3: Verification

Check: all customers visited exactly once, no vehicle exceeds capacity, all routes start and end at depot. Compare total distance against lower bound. Gate: All constraints satisfied, solution within 10% of lower bound.

Phase 4: Output

Return routes with sequence, distance, and load.

Output Format

json

{
  "routes": [{"vehicle": 1, "sequence": ["depot", "C3", "C7", "C1", "depot"], "distance_km": 45, "load": 850, "capacity": 1000}],
  "summary": {"total_distance_km": 180, "vehicles_used": 4, "utilization_avg": 0.82},
  "metadata": {"customers": 30, "method": "clarke_wright_2opt", "computation_ms": 150}
}

Examples

Show full SKILL.md (175 more words)Show less

Sample I/O

Input: 10 customers, 2 vehicles (cap=500), depot at center Expected: 2 routes, each serving ~5 customers, total distance minimized by geographic clustering.

Edge Cases

Input	Expected	Why
One customer demand > capacity	Infeasible or split delivery	Need split delivery VRP variant
All customers co-located	Minimal routing, capacity-limited trips	Distance is trivial, trips determined by load
Tight time windows	More vehicles needed	Time constraints may prevent full-capacity routes

Gotchas

Distance matrix quality: Road distance ≠ Euclidean distance. Use actual road network distances (Google Maps, OSRM) for practical routing.
Time windows add complexity: VRPTW (VRP with Time Windows) is significantly harder. Customers requiring specific delivery windows fragment routes.
Dynamic vs static: Real-world routing has cancellations, additions, and traffic. Plan static routes but allow dynamic re-optimization.
Driver constraints: Maximum driving hours, break requirements, and overtime costs add practical constraints not in the basic model.
Return to depot: Standard VRP assumes routes return to depot. Open VRP (routes end at last customer) needs different formulation.

References

For Clarke-Wright algorithm implementation, see references/clarke-wright.md
For metaheuristic approaches (SA, GA), see references/metaheuristics.md

Files

SKILL.md and 3 other files (references) in algo-sc-routing of asgard-ai-platform/skills.

SKILL.md
examples/sample_scenario.md
references/clarke-wright.md
references/metaheuristics.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Sc Routing 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.

Algo Sc Routing compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Algo Sc Routing this skillasgard-ai-platform/skills	242	—	~1.1k	Automated safety check: Pass	MIT	4 mo ago
Route Optimizerrevfactory/harness-100	1.3k	—	~1.2k	Automated safety check: Pass	Apache-2.0	6 mo ago
Ortools Pickup Delivery Routingbenchflow-ai/skillsbench	1.8k	—	~2k	Automated safety check: Pass	Apache-2.0	2 mo ago
SQL Optimizationgithub/awesome-copilot	40k	2 repos	~2.3k	Automated safety check: Pass	MIT	2 days ago
Matlab Solve Optimizationmatlab/matlab-agentic-toolkit	1.1k	—	~3.4k	Automated safety check: Pass	Custom licence	2 days ago
Agent Performance Optimizerruvnet/ruflo	74k	2 repos	~3.6k	Automated safety check: Pass	MIT	yesterday

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Questions about Algo Sc Routing

What does Algo Sc Routing do?

Solve vehicle routing problems to optimize delivery routes under capacity and time constraints. Algo Sc Routing is an agent skill from asgard-ai-platform/skills. Solve vehicle routing problems to optimize delivery routes under capacity and time constraints.

When should I use Algo Sc Routing?

Algo Sc Routing fits situations like: the user needs to plan delivery routes; minimize transportation costs; optimize fleet utilization — even if they say delivery route optimization; minimize driving distance.

How do I install Algo Sc Routing in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a claude-code`. Or copy the skill folder (algo-sc-routing in asgard-ai-platform/skills) into .claude/skills/algo-sc-routing in your project. Claude Code loads it when a task matches its description.

How do I install Algo Sc Routing in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-sc-routing -a codex`. Or copy the skill folder (algo-sc-routing in asgard-ai-platform/skills) into .agents/skills/algo-sc-routing in your project. Codex loads it when a task matches its description.

Can I use Algo Sc Routing 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 asgard-ai-platform/skills --skill algo-sc-routing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-sc-routing, .gemini/skills/algo-sc-routing, .github/skills/algo-sc-routing and .opencode/skills/algo-sc-routing in your project.

What does Algo Sc Routing need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Sc Routing is instructions for the agent only.

Does Algo Sc Routing 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 Algo Sc Routing 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 Algo Sc Routing use?

Algo Sc Routing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo Sc Routing use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.4k tokens, read only when the agent opens those files.

What are the alternatives to Algo Sc Routing?

Skills that share tags, products or a category with Algo Sc Routing: Route Optimizer (revfactory/harness-100, 1.3k stars), Ortools Pickup Delivery Routing (benchflow-ai/skillsbench, 1.8k stars), SQL Optimization (github/awesome-copilot, 40k stars) and Matlab Solve Optimization (matlab/matlab-agentic-toolkit, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Sc Routing?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

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

Install Algo Sc Routing

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Vehicle Routing Problem (VRP)

Overview

When to Use

Algorithm

Phase 1: Input Validation

Phase 2: Core Algorithm

Phase 3: Verification

Phase 4: Output

Output Format

Examples

Sample I/O

Edge Cases

Gotchas

References

Files

Compare with similar skills

Similar skills

More from asgard-ai-platform/skills

Questions about Algo Sc Routing