Agent skill

Primer Design

by K-Dense-AI in 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.

MITAuto-check passedResearch & Science

Install Primer Design

skills CLI
$ npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/scientific-agent-skills primer-design --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
primer-design
GitHub stars
48k
Used in
1 other repo
Token cost
~4.1k tokens
SKILL.md length
1,748 words
Files
15 (incl. scripts, references, assets)
Skills in repo
153
Repo updated
First seen
Licence
MIT

At a glance

Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates.

  • Works in 4 steps: Design candidates → Assess thermodynamics → Screen amplification products → …
  • Designing primer pairs
  • SKILL.md covers Choose the workflow, Establish the assay contract, Install and verify and 1. Design candidates, plus 5 more sections
  • Runs Python scripts from its folder; calls python and uv

What it does

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.

When your agent uses it

  • Designing primer pairs
  • Checking existing primers
  • Isoform-specific assays
  • Variant masking

Example prompts

  • “Use the primer-design skill to design and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target…”
  • “/primer-design”

Requirements

  • 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.

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Design candidates
  2. Assess thermodynamics
  3. Screen amplification products
  4. Validate the assay and deliver evidence

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 5 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • arxiv.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    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.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~131
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

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.

Safety

Auto-check passed

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.

SKILL.md

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.

Download SKILL.mdSave it as .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.
name
primer-design
description
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.
compatibility
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.
license
MIT
metadata.version
1.1
metadata.skill-author
K-Dense Inc.
metadata.last-reviewed
2026-10-01

Primer design and specificity

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.

Choose the workflow

RequestStart here
New genomic PCR or RT-qPCR pairDefine the assay and reference; design; assess thermodynamics; screen products.
Check an existing pairPrepare pair TSV; assess both full oligos and annealing cores; screen with explicit intended coordinates.
Exon junction, transcript isoform, allele discriminationRead design-workflows.md; supply sequence annotation before imposing constraints.
Cloning/adaptor-tailed primersDesign annealing cores, append declared 5-prime tails, reassess full oligos, reconstruct the final product.
Multiplex panelEnable --multiplex in both thermodynamics and specificity tools to assess oligo interactions and cross-pair products.
Degenerate, bisulfite, probe, or modified-base assayUse 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.

Establish the assay contract

Obtain what changes the result; use explicit provisional assumptions for an exploratory design, and identify them in the report:

  • Purpose and template: genomic DNA, cDNA, plasmid, or another defined substrate; target organism, accession with version, assembly/transcript release, strand, desired isoforms, and product-size range. Name wanted and unwanted templates.
  • Sequence evidence: local FASTA plus source/retrieval date and its SHA-256 hash. A locus excerpt uses local coordinates; record its mapping to the full reference. Include relevant paralogs, pseudogenes, alternate contigs, transcript isoforms, vector backbone, and host sequence in the appropriate screen.
  • Reaction conditions: polymerase/buffer, monovalent salt, total divalent salt, total dNTP, and oligo concentrations. Primer3 uses mM for salts/dNTP and nM for DNA. Record the initial reaction concentration separately from Primer3's effective annealing-oligo concentration parameter. Engine defaults are assumptions.
  • Constraints: target interval, allowed/excluded binding regions, junctions, variant exclusions and their source, fixed primers, tails, or multiplex membership. Do not guess exon boundaries or silently substitute another assembly.
  • Definition of an acceptable result: relevant off-target references, amplicon lengths, mismatch search limits, controls, and experimental validation appropriate to the assay. There is no universal thermodynamic cutoff that validates all PCRs.

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.

Install and verify

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.

bash
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" --help

Use 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:

bash
blastn -version
makeblastdb -version

Local 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.

1. Design candidates

For an initial functional demonstration, use the bundled synthetic sequence. It is nonbiological example input, not an experimentally validated assay:

bash
.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.tsv

For 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:

  • Passes validated sequence_args and global_args to Primer3. Unsupported or misspelled tags fail rather than silently changing the task.
  • Converts ambiguous template positions to N and forbids ambiguous primer bases. It does not interpret lowercase sequence as a repeat mask; use explicit exclusions.
  • Optionally imports --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.
  • Verifies returned forward and reverse sequences against the template and checks product lengths. Primer3's right-primer position is converted into a half-open binding interval; the reverse primer is already in ordering orientation.
  • Optionally appends --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.

2. Assess thermodynamics

Use the same chemistry as the design. The following explicit values match the bundled design defaults; replace them together when the actual conditions differ:

bash
.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.json

The 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.

3. Screen amplification products

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.

Show full SKILL.md (686 more words)Show less
bash
.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.json

This 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.

bash
.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.json

The 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.

Interpret the result
  • 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.

4. Validate the assay and deliver evidence

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:

  1. Exact ordering sequences, separated annealing cores and tails, primer lengths, intended product coordinates/size, and any relevant transcript/junction mapping.
  2. Chemistry and model settings; engine versions; reference accession/release, source, retrieval date, and file hashes; explicit exclusions and rationale.
  3. Intended and potential unintended products; uncertainty from unknown bases, incomplete references, heuristic searches, caps, and unsupported assay chemistry.
  4. Experimental evidence actually obtained, outstanding validation, and the reason for each recommended candidate. Label untested candidates accurately.

Validation and limits of this implementation

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.

Citing Scientific Agent Skills

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

Files

SKILL.md and 14 other files (scripts, references, assets) in skills/primer-design of K-Dense-AI/scientific-agent-skills.

  • SKILL.md
  • assets/assay-report-template.md
  • assets/demo-template.fasta
  • assets/qpcr-config.json
  • assets/requirements.txt
  • references/design-workflows.md
  • references/input-contract.md
  • references/sources.md
  • references/specificity.md
  • references/thermodynamics.md
  • scripts/_common.py
  • scripts/_specificity.py
  • scripts/check_thermodynamics.py
  • scripts/design_primers.py
  • scripts/screen_specificity.py

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

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.

Compare with similar skills

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Questions about Primer Design

What does Primer Design do?

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.

When should I use Primer Design?

Primer Design fits situations like: designing primer pairs; checking existing primers; isoform-specific assays; variant masking.

How do I install Primer Design in Claude Code?

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.

How do I install Primer Design in Codex?

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.

Can I use Primer Design in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Primer Design need to run?

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..

Does Primer Design access the network?

SKILL.md names 1 domain. As links in the text: arxiv.org. This is read from the text; nothing was executed.

Is Primer Design safe to install?

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.

What licence does Primer Design use?

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.

How many tokens does Primer Design use?

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.

What are the alternatives to Primer Design?

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.

Who maintains Primer Design?

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.