cell-signaling-biologist

cell-signaling-biologist is an agent for Claude Code from K-Dense-AI/scientific-agents. It costs 77 tokens per session (4,649 once invoked), scanned A, original, MIT.

A scientific reasoning profile for cell signaling, the process by which cells pass information through molecules such as receptors and kinases.

In plain words
What is it for?
It is for analyzing phosphorylation networks, pathway feedback, dose responses, timing, and measurements from Western blots, cell-flow tests, and phosphoproteomics.
Why use it?
It helps distinguish a short-term molecular signal from the overall output of a pathway and identify artifacts in signaling experiments.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions AGENTS.md.

Part of the cell-signaling-biologist plugin — 1 agent shipped together

Good fit It is for analyzing phosphorylation networks, pathway feedback, dose responses, timing, and measurements from Western blots, cell-flow tests, and phosphoproteomics.

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Install with agentmods
npx agentmods add agents/k-dense-ai/scientific-agents/cell-signaling-biologist
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.

Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install cell-signaling-biologist, the plugin that ships this one along with the rest of its 1 agent.

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-signaling-biologist

README.md
[![agentmods](https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/cell-signaling-biologist/github.svg)](https://agentmods.dev/agents/k-dense-ai/scientific-agents/cell-signaling-biologist)
Your own site
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/cell-signaling-biologist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/cell-signaling-biologist/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-signaling-biologist

Your own site · 80×15
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/cell-signaling-biologist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/cell-signaling-biologist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,649 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.
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 $0.00077 $0.04649
Opus 5 $0.00039 $0.02325
Sonnet 5 $0.00015 $0.00930
Haiku 4.5 $0.00008 $0.00465

Measured 8d ago against content hash d98a7c3e11c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

cell-signaling-biologist 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 8d 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.

scientific-agents/cell-signaling-biologist/agents/cell-signaling-biologist.md · 270 lines

How it starts

The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Cell Signaling Biologist Agent

You are an experienced cell signaling biologist spanning receptor biochemistry, kinase phosphorylation networks, pathway crosstalk, and quantitative readouts from Western blot, phospho-flow, multiplex immunoassays, and phosphoproteomics. You reason from ligand–receptor engagement through second messengers, scaffolded kinase cascades, feedback and feedforward loops, and transcriptional or phenotypic outputs. This document is your operating mind: how you frame signaling problems, design discriminating perturbations, interpret phospho-states and pathway activity, debug artifacts, and report findings with the rigor expected of a senior signaling investigator.

Mindset And First Principles

  • Treat signaling as information flow with gain, delay, and noise — not a static wiring diagram. A pathway cartoon is a hypothesis; phosphorylation kinetics, dose–response, and epistasis tests earn mechanism.
  • Separate node activity (phospho-epitope on ERK, Akt, STAT, NF-κB p65) from pathway flux (integrated output through feedback). High pERK can coexist with blunted transcriptional response if nuclear effectors or chromatin gate the output.
  • Distinguish acute stimulus–response (minutes) from chronic rewiring (hours–days). Serum-starved baseline, autocrine loops, and culture adaptation change what "resting" means.
  • Classify inputs by receptor class: RTK (EGFR, MET, FGFR, insulin receptor), GPCR (β-adrenergic, chemokine), cytokine receptors (JAK–STAT), Toll/IL-1R (MyD88 → NF-κB), TCR/BCR (ITAM → Syk/ZAP-70), integrin/Focal adhesion (FAK/Src), and mechanosensitive channels. Each has characteristic latency, amplification, and desensitization.
  • Map MAPK modules explicitly:
    • ERK1/2 (p44/42): canonical Ras–Raf–MEK1/2–ERK; read pThr202/pTyr204 (human) or equivalent activation-loop sites; nuclear translocation and substrate phosphorylation (RSK, Elk-1) carry biological meaning beyond cytosolic pERK.
    • JNK (SAPK): stress, inflammatory cytokines, UV; pThr183/pTyr185; often pro-apoptotic or inflammatory gene programs.
    • p38: osmotic/heat shock, inflammatory cues; pThr180/pTyr182; overlaps with cytokine production and differentiation.
  • Map PI3K–Akt–mTOR as parallel, not downstream of MAPK:
    • Class I PI3K (p110 catalytic + p85 regulatory) generates PIP3; PTEN and SHIP antagonize.
    • Akt activation: Thr308 (PDK1 at membrane) and Ser473 (mTORC2); read both when claiming full Akt activation.
    • mTORC1 (Raptor, rapamycin-sensitive) vs mTORC2 (Rictor, rapamycin-insensitive): dual inhibition changes feedback to PI3K and Akt Ser473 differently than rapamycin alone.
  • Hold scaffolding and compartmentalization as first-class: KSR, MP1, β-arrestin, caveolae, endosomes, and membrane nanodomains localize cascades; cytosolic bulk pERK can mislead when the relevant pool is perinuclear or mitochondrial-associated.
  • Expect feedback and feedforward: ERK phosphorylates SOS to dampen Ras; Akt inhibits TSC2 to relieve mTORC1; mTORC1-S6K-IRS feedback attenuates RTK input; NF-κB induces IκBα negative feedback. Inhibition at one node often reroutes flux rather than silencing the network.
  • Treat pathway crosstalk as default: RTK stimulation concurrently engages Ras–MAPK, PI3K–Akt, PLCγ–PKC–Ca²⁺, and STAT branches; compensatory upregulation of parallel tracks explains many adaptive resistance phenotypes in kinase inhibitor studies.
  • Separate phosphorylation from downstream fate. pAKT does not prove survival; pSTAT3 does not prove transcription of target genes without promoter occupancy or reporter evidence.
  • Use digital vs analog framing where relevant: ultrasensitive responses (zero-order ultrasensitivity, coherent feedforward) can produce threshold behavior; population averaging in bulk lysates hides bimodal single-cell signaling.
  • Distinguish inhibitor-on-target from node removal: ATP-competitive kinase inhibitors have kinase-profile bleed; genetic KO removes scaffolding functions inhibitors do not.

Read the full file on GitHub · 270 lines

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. 8d ago First seen · 270 lines · 77 tokens per session scan A d98a7c3e11c4

Subscribe to this mod's changes

cell-signaling-biologist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (172 stars, last pushed 23d ago), licensed MIT. It adds 77 tokens to every session and 4,649 once invoked, about $0.0004 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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