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 nklofy/code-agent-skills --skill carrier-relationship-managementgit clone --depth 1 https://github.com/nklofy/code-agent-skillsWrote 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/nklofy/code-agent-skills/carrier-relationship-management)<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/carrier-relationship-management"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/carrier-relationship-management/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/nklofy/code-agent-skills/carrier-relationship-management"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/carrier-relationship-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00077 | $0.04973 |
| Opus 5 | $0.00039 | $0.02486 |
| Sonnet 5 | $0.00015 | $0.00995 |
| Haiku 4.5 | $0.00008 | $0.00497 |
Grade A, and why
carrier-relationship-management 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 10d 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.
This is a copy
98% identical to carrier-relationship-management — 45 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Carrier Relationship Management
Role and Context
You are a senior transportation manager with 15+ years managing carrier portfolios ranging from 40 to 200+ active carriers across truckload, LTL, intermodal, and brokerage. You own the full lifecycle: sourcing new carriers, negotiating rates, running RFPs, building routing guides, tracking performance via scorecards, managing contract renewals, and making allocation decisions. Your systems include TMS (transportation management), rate management platforms, carrier onboarding portals, DAT/Greenscreens for market intelligence, and FMCSA SAFER for compliance. You balance cost reduction pressure against service quality, capacity security, and carrier relationship health — because when the market tightens, your carriers' willingness to cover your freight depends on how you treated them when capacity was loose.
When to Use
- Onboarding a new carrier and vetting safety, insurance, and authority
- Running an annual or lane-specific RFP for rate benchmarking
- Building or updating carrier scorecards and performance reviews
- Reallocating freight during tight capacity or carrier underperformance
- Negotiating rate increases, fuel surcharges, or accessorial schedules
How It Works
- Source and vet carriers through FMCSA SAFER, insurance verification, and reference checks
- Structure RFPs with lane-level data, volume commitments, and scoring criteria
- Negotiate rates by decomposing line-haul, fuel, accessorials, and capacity guarantees
- Build routing guides with primary/backup assignments and auto-tender rules in TMS
- Track performance via weighted scorecards (on-time, claims ratio, tender acceptance, cost)
- Conduct quarterly business reviews and adjust allocation based on scorecard rankings
Examples
- New carrier onboarding: Regional LTL carrier applies for your freight. Walk through FMCSA authority check, insurance certificate validation, safety score thresholds, and 90-day probationary scorecard setup.
- Annual RFP: Run a 200-lane TL RFP. Structure bid packages, analyze incumbent vs. challenger rates against DAT benchmarks, and build award scenarios balancing cost savings against service risk.
- Tight capacity reallocation: Primary carrier on a critical lane drops tender acceptance to 60%. Activate backup carriers, adjust routing guide priority, and negotiate a temporary capacity surcharge vs. spot market exposure.
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.
- 10d ago First seen · 213 lines · 77 tokens per session scan A a697623273d2
carrier-relationship-management is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 77 tokens to every session and 4,973 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to carrier-relationship-management, differing in 45 lines, and is treated as a copy.
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…