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 GGbond-bo/MemOmics-Agent --skill bioinformatics-html-reportgit clone --depth 1 https://github.com/GGbond-bo/MemOmics-AgentWrote 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/ggbond-bo/memomics-agent/bioinformatics-html-report)<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/bioinformatics-html-report"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/bioinformatics-html-report/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/ggbond-bo/memomics-agent/bioinformatics-html-report"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/bioinformatics-html-report.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 | $0.00023 | $0.04509 |
| Opus 5 | $0.00012 | $0.02254 |
| Sonnet 5 | $0.00005 | $0.00902 |
| Haiku 4.5 | $0.00002 | $0.00451 |
Grade A, and why
bioinformatics-html-report 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 9d 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 — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bioinformatics HTML Report Builder
A zero-dependency Python toolkit for generating publication-quality interactive HTML reports from bioinformatics analysis outputs (figures + tables).
规则N+1: 报告必须从日志自动填充（auto_fill_from_logs）
- 生成报告时，必须调用
collect_session_data(session_id)收集五层日志数据 - 然后调用
rb.auto_fill_from_logs(session_data)自动填充日志溯源 section - 不要仅凭 LLM 上下文记忆生成报告——会话过长时上下文会丢失
- 日志溯源 section 包括：工具调用记录、Skill经验日志、运行归档、辩论归档
- 如果 LLM 上下文中有分析内容（图表、参数等），仍然可以手动 add_figure/add_table
- 日志溯源是报告的必要部分，不是可选项
新增功能：日志溯源 auto_fill_from_logs()
from html_report_builder import ReportBuilder, collect_session_data
# 1. 收集本次会话的五层日志数据
session_data = collect_session_data(session_id="memomics-xxxxx")
# 2. 创建报告
rb = ReportBuilder(title="Analysis Report", ...)
# 3. 手动添加分析内容（图表、表格等）
rb.add_figure("result.png", title="UMAP", ...)
rb.add_table(...)
# 4. 自动填充日志溯源 section（从日志文件读取，不依赖 LLM 记忆）
rb.auto_fill_from_logs(session_data)
# 5. 保存
rb.save("output/report.html")
日志溯源会自动添加以下 section：
- 日志溯源：数据来源统计、会话元数据
- 工具调用记录：本次会话所有工具调用（从 state.db 读取）
- Skill经验日志：错误记录+修复方案（从 skills/logs/ 读取）
- 运行归档：每次运行的参数+结果（从 results/log/ 读取）
- 辩论归档：辩论完整记录（从 results/log/debate_*.json 读取）
规则N: 运行记录只是参考，不能跳过审查
- skill_evolution(action="query_logs") 返回的历史运行日志仅供参数参考
- 即使有 quality_score=9.0 的历史日志，仍必须执行 rail_review(pre)、debate_analysis、rail_review(post)
- 禁止因"之前跑过"而跳过任何审查步骤
- 禁止直接用历史日志里的脚本运行而不经本次审查
- 运行日志是"参考"不是"免审凭证"
What This Skill Provides
Three files:
| File | Purpose |
|---|---|
html_report_builder.py |
Core library — import this in your script |
example_usage.py |
Minimal template for any analysis (DEG, GSEA, etc.) |
hdwgcna_report_generator.py |
Full reference implementation for hdWGCNA |
references/sctour-report-template.md |
scTour trajectory report template (9 sections, 10 figs, 5 debates, 4 tables) |
references/figure-completeness-check.md |
Post-generation figure completeness verification and recovery procedure |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 456 lines · 23 tokens per session scan A a2d0e8e22476
bioinformatics-html-report is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 4,509 once invoked, about $0.0001 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.
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