Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/primer-design .claude/skills/primer-design && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .claude/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-designType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/primer-design .agents/skills/primer-design && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .agents/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/primer-design .cursor/skills/primer-design && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .cursor/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills.git --path skills/primer-design--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/primer-design .gemini/skills/primer-design && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .gemini/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-designInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/primer-design .github/skills/primer-design && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .github/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/primer-design .opencode/skills/primer-design && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "primer-design" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/primer-design into .opencode/skills/primer-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "primer-design", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
primer-designDesigns and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates.
Primer Design is an agent skill from K-Dense-AI/scientific-agent-skills. Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates. Use for designing primer pairs, checking existing primers, exon-junction or isoform-specific assays, variant masking, cloning tails, multiplex compatibility, and interpreting Primer-BLAST results. Includes bounded local in-silico PCR and BLAST screening; distinguishes computational candidates from experimentally validated assays.
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts, reference files and assets (for example `assets/assay-report-template.md`, `assets/qpcr-config.json` and `references/design-workflows.md`). Compatibility notes: Requires Python 3.11+ and primer3-py 2.3.1 for design and thermodynamics. Local exhaustive screening uses the standard library; BLAST screening additionally…
It sits in Research & Science. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 5 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonuvFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
arxiv.orgFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires Python 3.11+ and primer3-py 2.3.1 for design and thermodynamics. Local exhaustive screening uses the standard library; BLAST screening additionally needs blastn and makeblastdb on PATH. Network access is needed only for installation, reference retrieval, or external Primer-BLAST.
From compatibility in the SKILL.md frontmatter.
Primer Design loads about 4.1k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 131 tokens; SKILL.md has 1,748 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 1,748 words, ~4,089 tokens.
.claude/skills/primer-design/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.Produce candidate oligos in 5-prime-to-3-prime orientation, with the exact template, chemistry, intended products, and search scope behind each conclusion. Calculate sequence-dependent quantities with the supplied tools. A familiar gene name, good Primer3 penalty, or a single BLAST alignment cannot establish primer specificity.
| Request | Start here |
|---|---|
| New genomic PCR or RT-qPCR pair | Define the assay and reference; design; assess thermodynamics; screen products. |
| Check an existing pair | Prepare pair TSV; assess both full oligos and annealing cores; screen with explicit intended coordinates. |
| Exon junction, transcript isoform, allele discrimination | Read design-workflows.md; supply sequence annotation before imposing constraints. |
| Cloning/adaptor-tailed primers | Design annealing cores, append declared 5-prime tails, reassess full oligos, reconstruct the final product. |
| Multiplex panel | Enable --multiplex in both thermodynamics and specificity tools to assess oligo interactions and cross-pair products. |
| Degenerate, bisulfite, probe, or modified-base assay | Use the specialized workflow in design-workflows.md; the bundled ordinary-DNA model is insufficient. |
The local tooling supports paired primers with unambiguous ACGT cores. Advanced assay types have substantive design and validation guidance, but are not silently reduced to ordinary PCR. This skill designs assays; expression normalization, experimental diagnostic validation, and guide-RNA design are separate tasks.
Obtain what changes the result; use explicit provisional assumptions for an exploratory design, and identify them in the report:
Use input-contract.md for file schemas and coordinate examples. Copy assay-report-template.md into the analysis directory to collect evidence. Reference retrieval may be manual or through an established sequence API; preserve accession/version and verify the returned sequence. The supplied scripts use local files and do not submit sequences online.
Set SKILL_DIR to this skill's actual installed directory. Work in a separate analysis
directory so environments, reference databases, and results do not enter the skill.
uv venv --python 3.13 .venv-primer
uv pip install --python .venv-primer/bin/python -r "$SKILL_DIR/assets/requirements.txt"
.venv-primer/bin/python "$SKILL_DIR/scripts/design_primers.py" --helpUse the environment's Scripts/python.exe on Windows. The design/thermodynamic
examples target primer3-py 2.3.1, tested with Python 3.13. For the optional
BLAST engine, install NCBI BLAST+ from its official distribution and check:
blastn -version
makeblastdb -versionLocal integration checks also exercise BLAST+ 2.17.0; other releases require checking their output and search behavior before claiming equivalent coverage.
Read sources.md when updating dependencies or API assumptions. The scripts record engine versions and effective settings in their JSON reports.
For an initial functional demonstration, use the bundled synthetic sequence. It is nonbiological example input, not an experimentally validated assay:
.venv-primer/bin/python "$SKILL_DIR/scripts/design_primers.py" \
--template "$SKILL_DIR/assets/demo-template.fasta" \
--preset qpcr --config "$SKILL_DIR/assets/qpcr-config.json" \
--output design.json --pairs-out pairs.tsv --expected-out expected.tsvFor actual work, substitute the reviewed target FASTA and constraints. A multi-record
FASTA requires --record with its exact ID. The pcr preset requests 100–1000 bp;
qpcr requests 70–200 bp. Override these starting ranges in the configuration.
The example overrides its range to 90–180 bp and includes a specific target interval.
Multiple SEQUENCE_TARGET intervals are alternatives: Primer3 flanks at least one.
To require coverage of every interval, supply one enclosing target and verify the
returned product; separate assays require separate design runs.
The tool:
sequence_args and global_args to Primer3. Unsupported or
misspelled tags fail rather than silently changing the task.N and forbids ambiguous primer bases.
It does not interpret lowercase sequence as a repeat mask; use explicit exclusions.--mask-bed exclusions in the supplied template's coordinates.
Use BED intervals for variant/repeat masking only after validating the reference
mapping; it does not infer allele frequencies or convert arbitrary VCFs.--forward-tail and --reverse-tail to cores. The design score
and core Tm do not include those tails; step 2 checks complete oligos.Inspect engine_explanations when no candidates are found. Change a biologically
justified constraint and rerun; do not silently relax all constraints or return an
invented sequence. Preserve previous reports when comparing parameter choices.
expected.tsv describes intended products on the design template. If screening
another reference, map these coordinates to its exact record IDs and orientation.
A cDNA product cannot be relabeled as a genomic interval across introns.
Use the same chemistry as the design. The following explicit values match the bundled design defaults; replace them together when the actual conditions differ:
.venv-primer/bin/python "$SKILL_DIR/scripts/check_thermodynamics.py" \
--pairs pairs.tsv --mv-conc 50 --dv-conc 1.5 --dntp-conc 0.6 \
--dna-conc 50 --temp-c 37 --output thermodynamics.jsonThe report contains core Tm, core and full-oligo hairpins/homodimers, full-oligo
heterodimers, self 3-prime end stability, and both directional inter-oligo 3-prime
end-stability calculations. Delta-G and
delta-H are reported in kcal/mol; delta-S in cal/(mol K). --temp-c controls the
temperature for delta-G, not a recommended PCR annealing temperature.
If design used different Tm/salt models, also set --tm-method and
--salt-corrections-method to match; see the mapping in the input contract.
For a panel, add --multiplex to examine all unordered oligo combinations, including
forward/forward and reverse/reverse between different pairs. Distinguish a panel to
be combined from alternative candidates that will be tested separately.
Read thermodynamics.md before interpreting these values. Full oligos longer than 60 bases are reported as unresolved; they are never silently truncated. Modified bases and degenerate mixtures require a suitable model. Thermodynamic predictions support ranking and experimental planning, not a blanket claim of primer quality. Nonfinite Tm or a Tm at/below absolute zero is rejected as a calculation/input failure; such a result must not be ranked as an ordinary low-Tm primer.
Read specificity.md before making a specificity claim. Search each primer on both strands and pair inward-facing binding sites. A binding hit alone is not an amplicon; absence of a reported hit is not proof of absence.
.venv-primer/bin/python "$SKILL_DIR/scripts/screen_specificity.py" \
--pairs pairs.tsv --reference "$SKILL_DIR/assets/demo-template.fasta" \
--expected expected.tsv --engine exhaustive \
--min-product 40 --max-product 1000 --max-mismatches 2 \
--three-prime-bases 5 --max-three-prime-mismatches 0 \
--output specificity.jsonThis enumerates full-length, ungapped binding sites in a bounded local reference and checks F/R, R/F, F/F, and R/R products for each pair. Reference ambiguity is treated conservatively as unresolved sequence; it cannot establish a clean result. The mismatch limits are a search model, not a validated polymerase discrimination rule. Tight 3-prime thresholds can exclude amplifiable mismatched sites; broaden the search when evaluating that uncertainty.
Use --circular RECORD_ID for a circular molecule. Wrapped products use canonical
start coordinates and unwrapped ends greater than the reference length; only
products spanning at most one molecule are considered. Caps on comparisons, hits,
and products prevent unbounded work; hitting a cap makes the result incomplete.
.venv-primer/bin/python "$SKILL_DIR/scripts/screen_specificity.py" \
--pairs pairs.tsv --reference "$SKILL_DIR/assets/demo-template.fasta" \
--expected expected.tsv --engine blast \
--min-product 40 --max-product 1000 --max-mismatches 2 \
--three-prime-bases 5 --max-three-prime-mismatches 0 \
--output specificity-blast.jsonThe script builds a temporary local BLAST database, runs short-query alignment, rechecks complete primer-length candidate sites, and records commands and versions. It checks configured hit limits and tool failures. BLAST discovery is heuristic: successful execution and unsaturated limits do not establish exhaustive coverage. The reference is loaded in memory; plan memory and search bounds for large genomes. The small example verifies execution, not human-genome sensitivity or scalability.
For broader public-reference screening, follow the official NCBI Primer-BLAST workflow in specificity.md, choosing the organism, database, intended templates, maximum product length, and mismatch policy deliberately. Submitting a sequence sends it to NCBI; keep local-only work local. Primer-BLAST is not a documented REST API implemented by these scripts.
potential_off_target: inspect every unexpected product, including its orientation,
length, mismatch positions, and reference identity; redesign or justify its relevance.intended_target_not_found: resolve mapping, reference, sequence, and search problems
before drawing specificity conclusions.no_expected_target: results are a product inventory, without a target-specific
conclusion. Supply all intended product intervals for the assay.incomplete: reference uncertainty, resource limits, or failed computation leaves
the conclusion unresolved. Inspect the reported reason and repeat appropriately.no_off_target_found_within_search_scope: state the exact reference and search
model. Check the report's exhaustive/heuristic flags; this is not empirical validation.For a multiplex reaction, add --multiplex to the specificity command as well as
the thermodynamics command. The specificity tool then searches every cross-pair
oligo combination, retaining the original oligo identities and tails. It treats
cross-pair products as unintended; intentionally shared-primer designs require
explicit standalone combinations with reviewed expected products. The
--max-panel-combinations cap defaults to 10,000 and is checked before expansion.
Thermodynamic --multiplex checks structures; specificity --multiplex checks
reference products. Both are necessary for this panel assessment.
Select candidates using assay purpose, coverage, specificity evidence, and chemistry, not just the lowest Primer3 penalty. Preserve multiple candidates when uncertainty remains. Follow design-workflows.md for the relevant experimental checks: expected product identity/size, negative and no-template controls, genomic contamination controls for RT-qPCR, and efficiency/dynamic-range assessment when quantification is intended. A single melt peak alone does not prove identity.
Deliver the completed report template, pair TSV, design/thermodynamic/specificity JSON, and source manifest. Include:
The repository suite at tests/primer-design/ exercises actual Primer3 calculations,
orientation and coordinate reconstruction, masking, tails, concentration handling,
off-target product geometry, mismatch/ambiguity behavior, failure states, and local
BLAST integration when its executables are installed. Synthetic fixtures establish
software behavior; they do not validate a biological assay or prove genome-wide recall.
Tools return 0 when their computation completes, 1 for a completed design with no candidates or incomplete long-oligo thermodynamics, and 2 for invalid input or tool failure. Read the JSON scientific status even after exit 0: finding an off-target is a successfully completed calculation. See the input contract for screen-specific exits.
If used in published work, cite the upstream methods in sources.md and Scientific Agent Skills. Report the software versions and assay-specific settings required to reproduce the actual analysis.
© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 14 other files (scripts, references, assets) in skills/primer-design of K-Dense-AI/scientific-agent-skills.
Open the folder on GitHubat commit 92ace75
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.
Primer Design next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Primer Design this skillK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.1k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
K-Dense-AI/scientific-agent-skills
Plans, runs, and documents analytical method validation, verification, or transfer studies under ICH Q2(R2)/Q14, USP, ICH M10, CLSI EP, or ISO/IEC 17025.
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
K-Dense-AI/scientific-agent-skills
Plans and audits runs of the HypoGeniC and HypoRefine packages, which propose hypotheses from labeled text datasets, with local checks before any model call.
K-Dense-AI/scientific-agent-skills
Organizes scope, controlled documents, risk files and traceability into draft evidence for human review against ISO 13485, 14971, 17025 and 15189.
Categories
Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates. Primer Design is an agent skill from K-Dense-AI/scientific-agent-skills. Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates.
Primer Design fits situations like: designing primer pairs; checking existing primers; isoform-specific assays; variant masking.
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a claude-code`. Or copy the skill folder (skills/primer-design in K-Dense-AI/scientific-agent-skills) into .claude/skills/primer-design in your project. Claude Code loads it when a task matches its description.
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a codex`. Or copy the skill folder (skills/primer-design in K-Dense-AI/scientific-agent-skills) into .agents/skills/primer-design in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/primer-design, .gemini/skills/primer-design, .github/skills/primer-design and .opencode/skills/primer-design in your project.
Going by SKILL.md and its folder, Primer Design needs Python for the scripts in its folder and the command-line tools its instructions call (python and uv). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Python 3.11+ and primer3-py 2.3.1 for design and thermodynamics. Local exhaustive screening uses the standard library; BLAST screening additionally needs blastn and makeblastdb on PATH. Network access is needed only for installation, reference retrieval, or external Primer-BLAST..
SKILL.md names 1 domain. As links in the text: arxiv.org. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Primer Design is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Primer Design: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.
Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.