cell-cell-communication

cell-cell-communication is a skill for Claude Code from GGbond-bo/MemOmics-Agent. It costs 4 tokens per session (4,977 once invoked), scanned A, original, MIT.

A single-cell analysis workflow for studying communication between cells using their gene-expression profiles.

In plain words
What is it for?
Use it to investigate possible signaling relationships among cell populations in single-cell RNA sequencing data.
Why use it?
It provides a structured process for checking methods, parameters, and results during cell-interaction analysis.

Skill for Claude Code

Written for Claude Code: when-to-use in frontmatter.

Good fit Use it to investigate possible signaling relationships among cell populations in single-cell RNA sequencing data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ggbond-bo/memomics-agent/cell-cell-communication
Install

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.

Any agent
npx skills add GGbond-bo/MemOmics-Agent --skill cell-cell-communication
Clone the repo
git clone --depth 1 https://github.com/GGbond-bo/MemOmics-Agent

Made for: Claude Code.

Wrote 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.

agentmods badge for cell-cell-communication

README.md
[![agentmods](https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/cell-cell-communication/github.svg)](https://agentmods.dev/skills/ggbond-bo/memomics-agent/cell-cell-communication)
Your own site
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/cell-cell-communication"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/cell-cell-communication/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.

agentmods 80×15 button for cell-cell-communication

Your own site · 80×15
<a href="https://agentmods.dev/skills/ggbond-bo/memomics-agent/cell-cell-communication"><img src="https://agentmods.dev/badge/skills/ggbond-bo/memomics-agent/cell-cell-communication.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 4 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,977 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00004 $0.04977
Opus 5 $0.00002 $0.02488
Sonnet 5 $0.00001 $0.00995
Haiku 4.5 $0.00000 $0.00498

Measured 9d ago against content hash c6eb8104c445, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

cell-cell-communication 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.

hermes_home/skills/bioinformatics/cell-cell-communication/SKILL.md · 349 lines

How it starts

The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.


⛔ MemOmics 强制规则(不可违反,优先级最高)

本 skill 已集成到 MemOmics-Agent 自进化生信分析平台。以下规则覆盖所有 Biomni 默认行为。

规则1: 拿到数据 → 必须调 search_knowledge

  • 每个分析步骤写代码前,必须先调 search_knowledge(species=..., tissue=..., direction=..., query="<步骤名> 参数")
  • 知识库有匹配 → 用知识库的参数和模板
  • 知识库无匹配 → 用 web 搜索文献,提取方法和参数,存入知识库
  • 绝对不能跳过直接写代码

规则2: 7步循环(每步必须走完整循环)

1. search_knowledge 查本步骤的方法和参数
2. check_env 检查环境
3. rail_review(pre) 前置审查
4. source/import 预写脚本(禁止 inline 代码)
5. terminal 执行(分步执行,禁止 && 连接多步骤)
6. debate_analysis 多方辩论(正方/反方切断上下文独立生成 + LLM裁决)
7. rail_review(post) 后置审查

规则3: 代码分段执行 — 写一步跑一步

  • 禁止一次性写完全部代码用 && 连接执行
  • 必须分步:写一步 → 执行 → 检查结果 → 辩论 → 下一步

规则4: 关键参数多参数尝试 + 辩论

  • 涉及数值参数时(如 resolution, n_pcs, min_features, FDR threshold等),至少尝试 2-3 个值
  • 每次参数变更后调 debate_analysis 辩论"这个参数合理吗?结果有没有变好?"
  • 辩论格式:正方(支持当前参数)vs 反方(质疑+替代方案)→ 裁判决断
  • 不确定的参数就辩论,不要自己拍脑袋

规则5: 执行后审查

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"

    • 图片检查
      • 图有没有生成?没生成 → 强制重新执行
      • 图片是否空白(全白/全黑/全单一色)?空白 → 强制重新出图
      • 图片是否有 NA/缺失值(>10% 像素是 NA)?有 NA → 强制重新出图
      • 图片大小是否过小(<5KB)?过小 → 强制重新出图
      • 图片数量是否足够?(每步至少 1 张图,关键步骤至少 2-3 张)
    • 代码质量检查
      • 代码行数是否合理?(过短可能偷懒,过长可能未分段)
      • 代码是否有注释?
      • 代码是否分段执行(禁止 && 连接多步骤)?
    • 结果合理性
      • 数值范围是否合理?
      • 跟知识库对应吗?
    • 参数和结论辩论
      • 有参数的选择 → 必须调 debate_analysis 辩论
      • 有结论输出 → 必须调 debate_analysis 辩论
      • 不通过 → 修复重跑
      • 通过 → 必须调 skill_evolution(action="record_run") 记录成功经验(skill_name/script_name/species/tissue/direction/params_used/result_summary/quality_score/notes) → 创建目录存储(figures/results/scripts/data) → 下一步
      • 不通过 → 修复后重跑 → 成功后调 skill_evolution(action="record_run");如果是脚本报错 → 调 skill_evolution(action="record_error") 记录根因+修复方案

规则6: 结果存储结构

results/<模块>/<方法>/
  ├── scripts/     # 分析脚本
  ├── figures/     # PNG + SVG 图表
  ├── data/        # RDS/H5AD 中间数据
  └── results/     # CSV/TSV 结果表

Read the full file on GitHub · 349 lines

Files

What ships with it

8 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.

Changes

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.

  1. 9d ago First seen · 349 lines · 4 tokens per session scan A c6eb8104c445

Subscribe to this mod's changes

cell-cell-communication is a skill published in the GitHub repository GGbond-bo/MemOmics-Agent (19 stars, last pushed 2d ago), licensed MIT. It adds 4 tokens to every session and 4,977 once invoked, about $0.0000 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.

Related

Other skills, from other repositories

sc-cell-communication

Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R). Skip when assigning cell-type labels (use sc-cell-annotation); transcription factor → target regulatory networks (use sc-grn).

TianGzlab/OmicsClaw · 69 tokens

sc-enrichment

Load when running bulk-style pathway enrichment (ORA / GSEA / GSEA-R / GSVA-R) on a per-group ranked DE / marker list against a gene-set library. Skip when computing per-cell pathway scores in-place (use sc-pathway-scoring); de-novo gene-program discovery (use sc-gene-programs).

TianGzlab/OmicsClaw · 71 tokens

sc-grn

Load when inferring TF → target gene regulatory networks on a normalised scRNA AnnData via pySCENIC (GRNBoost2 + cisTarget + AUCell) or correlation-based GRN fallback (when arboreto is unavailable, in --demo, or with --allow-simplified-grn). Skip when computing ligand-receptor cell-cell signalling (use…

TianGzlab/OmicsClaw · 100 tokens

sc-pathway-scoring

Load when computing per-cell pathway / gene-set scores on a normalised scRNA AnnData via AUCell (R or Python) or Scanpy scoregenes. Skip when running condition-vs-control bulk-style enrichment on top of a DE table (use sc-enrichment); de-novo gene-program discovery (use sc-gene-programs).

TianGzlab/OmicsClaw · 75 tokens

sc-batch-integration

Load when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects. Skip when the data is one sample (no batch effect to integrate); upstream merging only (use sc-multi-count).

TianGzlab/OmicsClaw · 70 tokens

sc-cell-annotation

Load when assigning cell-type labels to a clustered scRNA AnnData via marker dictionaries, CellTypist, PopV, KNNPredict, SingleR, scmap, SCSA, or a manual cluster-to-label map. Skip when ranking marker genes per cluster (use sc-markers); condition-vs-control DE (use sc-de).

TianGzlab/OmicsClaw · 73 tokens