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 LeonChaoX/qinyan-academic-skills --skill depmapgit clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-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/leonchaox/qinyan-academic-skills/depmap)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/depmap"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/depmap/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/leonchaox/qinyan-academic-skills/depmap"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/depmap.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 | $0.00052 | $0.02701 |
| Opus 5 | $0.00026 | $0.01350 |
| Sonnet 5 | $0.00010 | $0.00540 |
| Haiku 4.5 | $0.00005 | $0.00270 |
Grade A, and why
depmap scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url, params=params) This is a copy
94% identical to depmap — 3 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DepMap — Cancer Dependency Map
Overview
The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:
- Identifying which genes are essential for specific cancer types
- Finding cancer-selective dependencies (therapeutic targets)
- Validating oncology drug targets
- Discovering synthetic lethal interactions
Key resources:
- DepMap Portal: https://depmap.org/portal/
- DepMap data downloads: https://depmap.org/portal/download/all/
- Python package:
depmap(or access via API/downloads) - API: https://depmap.org/portal/api/
When to Use This Skill
Use DepMap when:
- Target validation: Is a gene essential for survival in cancer cell lines with a specific mutation (e.g., KRAS-mutant)?
- Biomarker discovery: What genomic features predict sensitivity to knockout of a gene?
- Synthetic lethality: Find genes that are selectively essential when another gene is mutated/deleted
- Drug sensitivity: What cell line features predict response to a compound?
- Pan-cancer essentiality: Is a gene broadly essential across all cancer types (bad target) or selectively essential?
- Correlation analysis: Which pairs of genes have correlated dependency profiles (co-essentiality)?
Core Concepts
Dependency Scores
| Score | Range | Meaning |
|---|---|---|
| Chronos (CRISPR) | ~ -3 to 0+ | More negative = more essential. Common essential threshold: −1. Pan-essential genes ~−1 to −2 |
| RNAi DEMETER2 | ~ -3 to 0+ | Similar scale to Chronos |
| Gene Effect | normalized | Normalized Chronos; −1 = median effect of common essential genes |
Key thresholds:
- Chronos ≤ −0.5: likely dependent
- Chronos ≤ −1: strongly dependent (common essential range)
Cell Line Annotations
What ships with it
1 file 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.
- 7d ago First seen · 301 lines · 52 tokens per session scan A 3bd80d251776
depmap is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (880 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 2,701 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to depmap, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
academic-research
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clean-data
Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher…
model-scaffold
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a…
model-sourcing
Vet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation…
architecture-zoo
Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a…
mllm-eval
Design or audit a model-agnostic evaluation harness for an LLM or multimodal LLM on a clinical task (radiology report generation, visual question answering, clinical text extraction/classification) — the adjudicated reference standard, clinical-efficacy metrics (RadGraph-F1 / CheXbert-F1 beyond BLEU/ROUGE)…