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.
git clone --depth 1 https://github.com/yujiachen-y/agent-bundleWrote 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/commands/yujiachen-y/agent-bundle/quick-analysis)<a href="https://agentmods.dev/commands/yujiachen-y/agent-bundle/quick-analysis"><img src="https://agentmods.dev/badge/commands/yujiachen-y/agent-bundle/quick-analysis/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/commands/yujiachen-y/agent-bundle/quick-analysis"><img src="https://agentmods.dev/badge/commands/yujiachen-y/agent-bundle/quick-analysis.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 | $0.00012 | $0.00126 |
| Opus 5.5 | $0.00005 | $0.00050 |
| Sonnet 5.5 | $0.00002 | $0.00025 |
| Haiku 4.5 | $0.00001 | $0.00013 |
Grade A, and why
quick-analysis 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 1mo 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.
What it actually says
Quick Financial Analysis
Perform a quick financial analysis based on the user's request: $ARGUMENTS
Steps
- Identify the type of analysis needed (variance, trend, comparison, etc.)
- Create a Python script in
/workspace/analysis.pythat performs the analysis - Run the script in the sandbox
- Format the results using the report-formatter skill guidelines
- Present findings with an executive summary, data table, and recommendations
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.
- 1mo ago First seen · 18 lines · 12 tokens per session scan A 4e78ce1c6b46
quick-analysis is a command published in the GitHub repository yujiachen-y/agent-bundle (10 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 126 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
cost-report
Generate a local Claude Code cost report from the ECC cost-tracker metrics log.
pm-pricing
Author or refresh a pricing plan — 2–3 monetization models with willingness-to-pay rationale per audience. Spawns pricing-strategist.
pm-burn
Cost burn-rate — LLM cost over the last N days vs a human-equivalent baseline (savingsx). Flags cost outliers and abnormal-burn agents.
flow-nexus-payments
Credit management, billing, and payment configuration.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.