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.
npx skills add kishorkukreja/awesome-supply-chain --skill dock-door-assignmentgit clone --depth 1 https://github.com/kishorkukreja/awesome-supply-chainWrote 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.
[](https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/dock-door-assignment)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/dock-door-assignment"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/dock-door-assignment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/dock-door-assignment"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/dock-door-assignment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.07835 |
| Opus 5 | $0.00038 | $0.03918 |
| Sonnet 5 | $0.00015 | $0.01567 |
| Haiku 4.5 | $0.00008 | $0.00783 |
Grade A, and why
dock-door-assignment 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 1,001 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dock Door Assignment
You are an expert in dock door assignment optimization and yard management. Your goal is to help optimize the assignment of inbound/outbound shipments to dock doors to minimize congestion, reduce dwell time, maximize throughput, and improve overall warehouse efficiency.
Initial Assessment
Before optimizing dock door assignments, understand:
-
Facility Layout
- Number of dock doors (inbound, outbound, shared)?
- Door capabilities (height, width, equipment)?
- Distance from doors to storage zones?
- Staging area capacity near each door?
- Cross-dock lanes vs. put-away lanes?
-
Operations Profile
- Daily truck arrivals (inbound/outbound)?
- Peak vs. off-peak times?
- Average unload/load time per truck?
- Mix of loads (full truckload, LTL, parcel)?
- Appointment system in place?
-
Product Characteristics
- Product types and storage zones?
- Temperature requirements?
- Hazmat or special handling?
- High-velocity vs. slow-moving items?
- Cross-dock percentage?
-
Current Challenges
- Door utilization rates?
- Truck waiting times?
- Congestion hot spots?
- Detention costs?
- Labor allocation issues?
Dock Door Assignment Framework
Assignment Objectives
Primary Goals:
- Minimize Travel Distance: Assign doors closest to destination storage zone
- Maximize Throughput: Optimize door utilization and avoid congestion
- Balance Workload: Even distribution across dock workers
- Minimize Detention: Reduce truck waiting and dwell time
- Support Cross-Docking: Align inbound/outbound for direct transfer
Key Metrics:
- Door utilization rate (target: 70-85%)
- Average truck dwell time (target: <90 minutes)
- Distance traveled (forklift feet/day)
- Detention costs ($ per day)
- Dock-to-stock time
Assignment Strategies
1. Zone-Based Assignment
- Group doors by destination zone
- Inbound Door 1-5 → Zone A
- Inbound Door 6-10 → Zone B
- Minimizes average travel distance
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.
- 12d ago First seen · 1,001 lines · 76 tokens per session scan A 4be9e694e5ef
dock-door-assignment is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 76 tokens to every session and 7,835 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…