Borrowing it
Nothing to install: this file belongs to bheadwei/claude-GUNDAM-zh-tw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bheadwei/claude-GUNDAM-zh-tw/main/.claude/skills/cost-aware-llm-pipeline/SKILL.mdgit clone --depth 1 https://github.com/bheadwei/claude-GUNDAM-zh-twWrote 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/bheadwei/claude-gundam-zh-tw/cost-aware-llm-pipeline)<a href="https://agentmods.dev/skills/bheadwei/claude-gundam-zh-tw/cost-aware-llm-pipeline"><img src="https://agentmods.dev/badge/skills/bheadwei/claude-gundam-zh-tw/cost-aware-llm-pipeline/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/bheadwei/claude-gundam-zh-tw/cost-aware-llm-pipeline"><img src="https://agentmods.dev/badge/skills/bheadwei/claude-gundam-zh-tw/cost-aware-llm-pipeline.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 | $0.00123 | $0.01719 |
| Opus 5 | $0.00062 | $0.00860 |
| Sonnet 5 | $0.00025 | $0.00344 |
| Haiku 4.5 | $0.00012 | $0.00172 |
Grade A, and why
cost-aware-llm-pipeline 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 3d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 3d ago Changed · -4 lines · +90 tokens per session 2b5a721e15d4
- 9d ago First seen · 184 lines · 33 tokens per session scan A 9c2a24ce48d1
cost-aware-llm-pipeline is a skill published in the GitHub repository bheadwei/claude-GUNDAM-zh-tw (11 stars, last pushed yesterday), with no licence file. It adds 123 tokens to every session and 1,719 once invoked, about $0.0006 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 2026-09-03.
Other skills, from other repositories
ai-readiness
Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual…
llm-cost-advisor
WHAT — Recommend the most cost-effective LLM provider for a given task type. Shows estimated cost per run across available providers and integrates with devcompanion llm-status to show what is actually available.
token-cost-estimator
Use this skill before running any prompt in production or sharing a workflow with stakeholders. Triggers on phrases like "how much will this cost", "compare model costs", "which model should I use", "estimate tokens", "pre-flight check", or when a user pastes a prompt and asks about inference economics. Takes a prompt…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
tinker-training-cost
Calculates training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.