Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill qiskitgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-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/k-dense-ai/scientific-agent-skills/qiskit)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/qiskit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/qiskit/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/k-dense-ai/scientific-agent-skills/qiskit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/qiskit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00075 | $0.02869 |
| Opus 5 | $0.00037 | $0.01435 |
| Sonnet 5 | $0.00015 | $0.00574 |
| Haiku 4.5 | $0.00007 | $0.00287 |
Grade A, and why
qiskit 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.
How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qiskit
Use current Qiskit 2.x APIs to build circuits, prepare hardware-compatible instruction set architecture (ISA) circuits, and execute them through V2 primitives.
This skill was verified on 2026-07-23 against the PyPI releases qiskit==2.5.0, qiskit-ibm-runtime==0.48.0, and qiskit-aer==0.17.2. Check references/sources.md before changing pins or documenting newly released behavior.
Choose the Right Path
| Goal | Recommended interface |
|---|---|
| Exact local sampling | qiskit.primitives.StatevectorSampler |
| Exact local expectation values | qiskit.primitives.StatevectorEstimator |
| High-performance or noisy simulation | Qiskit Aer |
| IBM QPU sampling | qiskit_ibm_runtime.SamplerV2 |
| IBM QPU expectation values and mitigation | qiskit_ibm_runtime.EstimatorV2 |
| Backend without native primitives | BackendSamplerV2 or BackendEstimatorV2 |
| Open-system or master-equation dynamics | Prefer QuTiP |
| Differentiable quantum machine learning | Prefer PennyLane unless Qiskit integration is required |
Installation
Create an isolated environment and install only the components needed:
uv venv --python 3.13
source .venv/bin/activate
# Core SDK plus plotting support
uv pip install "qiskit[visualization]==2.5.0"
# Add only when needed
uv pip install "qiskit-ibm-runtime==0.48.0"
uv pip install "qiskit-aer==0.17.2"
Do not install qiskit-terra; it was superseded by the qiskit distribution. Qiskit Runtime, Aer, Nature, Machine Learning, Optimization, and Algorithms are separate distributions.
For IBM account setup, CI-safe credential handling, optional packages, and environment repair, read references/setup.md.
Core Workflow
Follow this sequence for every hardware-oriented workload:
- Map the problem to a circuit and, for Estimator, one or more observables.
- Optimize the parameterized circuit once for the selected backend.
- Apply the layout to every observable.
- Execute ISA circuits through a V2 primitive using Primitive Unified Blocs (PUBs).
- Analyze register-aware results, metadata, uncertainty, and resource usage.
What ships with it
14 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.
- references/algorithms.md 9.7 KB
- references/backends.md 11 KB
- references/circuits.md 9.3 KB
- references/migration.md 9.6 KB
- references/patterns.md 9.1 KB
- references/primitives.md 12 KB
- references/setup.md 7.9 KB
- references/sources.md 9.1 KB
- references/testing.md 10 KB
- references/transpilation.md 11 KB
- references/visualization.md 7.9 KB
- scripts/check_environment.py 7.7 KB runs code
- scripts/inspect_runtime.py 6.3 KB runs code
- scripts/run_local_primitives.py 5.5 KB runs code
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.
- 8d ago First seen · 277 lines · 75 tokens per session scan A ecce52d2f699
qiskit is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 75 tokens to every session and 2,869 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.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.