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Primer Design

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Tool integration
  • Science and research
  • MIT
  • Low risk
  • Has scripts

From the author

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.
— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, reproducible workflow for the design and specificity assessment of PCR and RT-qPCR primers. It integrates Primer3 for thermodynamic design and local BLAST or exhaustive search tools for off-target amplification screening. The workflow emphasizes rigorous input contracts, explicit thermodynamic assumptions, and the generation of traceable evidence reports, ensuring that all design decisions are documented and verifiable without relying on external web-based services.

What it does

  • Designs PCR and RT-qPCR primer pairs based on user-defined templates and thermodynamic constraints.
  • Audits existing primer pairs for thermodynamic stability and potential off-target amplification.
  • Performs exhaustive local in-silico PCR and BLAST-based specificity screening.
  • Supports advanced assay requirements including exon-junction targeting, multiplexing, and cloning tails.
  • Generates standardized JSON reports and assay documentation for experimental validation.
  • Enforces strict input validation to prevent silent failures or incorrect assumptions.

When to use it

  • When you need to design new primers for genomic PCR, RT-qPCR, or cloning applications.
  • When you must verify the specificity and thermodynamic properties of existing primer sequences.
  • When working in environments requiring local-only processing of sensitive or proprietary sequence data.
  • When you need to document the full rationale, assumptions, and evidence for a primer design project.

Advantages

  • Ensures reproducibility by requiring explicit configuration, reference versions, and chemistry settings.
  • Provides local, offline screening capabilities to maintain data privacy and control.
  • Includes robust checks for off-target products and multiplex interactions.
  • Facilitates systematic validation by separating computational candidates from experimental requirements.

Usage examples

“Design a new qPCR primer pair for the target sequence in demo-template.fasta using the qpcr preset.”
The agent executes the design script with the specified template and configuration, producing a JSON report of candidate pairs and their associated thermodynamic properties.
“Check the specificity of an existing primer pair against my local reference genome.”
The agent runs the specificity screening tool against the provided reference, identifying potential off-target binding sites and reporting product lengths and mismatch counts.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/k-dense-ai/primer-design/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip primer-design.zip -d .agents/skills

Documentation by K-Dense Inc.

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.

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:

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:

.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:

.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.

.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.

.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.

Files

scripts/screen_specificity.py

#!/usr/bin/env python3
"""Screen primers against an explicit local FASTA with reproducible search bounds."""

from __future__ import annotations

import argparse
import platform
import sys
from datetime import datetime, timezone
from pathlib import Path

sys.dont_write_bytecode = True

from _common import check_output_paths, read_fasta, read_pairs, sha256_file, write_json
from _specificity import blast_hits, enumerate_products, exhaustive_hits, expand_panel_pairs, read_expected


def build_parser():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--pairs", required=True, type=Path, help="TSV: pair_id, forward, reverse; optional forward_tail, reverse_tail")
    parser.add_argument("--reference", required=True, type=Path, help="FASTA with unique record IDs")
    parser.add_argument("--output", required=True, type=Path, help="JSON report; incomplete runs exit 2")
    parser.add_argument("--expected", type=Path, help="TSV: pair_id, record_id, start, end (0-based half-open)")
    parser.add_argument("--engine", choices=("exhaustive", "blast"), default="exhaustive")
    parser.add_argument("--multiplex", action="store_true", help="Also screen every cross-pair oligo combination; all cross-pair products are potential off-targets")
    parser.add_argument("--max-panel-combinations", type=int, default=10_000, help="Cross-pair oligo combination cap, checked before panel expansion")
    parser.add_argument("--circular", action="append", default=[], metavar="RECORD_ID", help="Repeat for circular records; intervals can end beyond record length")
    parser.add_argument("--min-product", type=int, default=40)
    parser.add_argument("--max-product", type=int, default=2000)
    parser.add_argument("--max-mismatches", type=int, default=2)
    parser.add_argument("--three-prime-bases", type=int, default=5)
    parser.add_argument("--max-three-prime-mismatches", type=int, default=0)
    parser.add_argument("--max-comparisons", type=int, default=100_000_000, help="Base comparison cap; exceeding a cap fails closed")
    parser.add_argument("--max-hits", type=int, default=100_000)
    parser.add_argument("--max-products", type=int, default=100_000, help="Per-pair product cap")
    parser.add_argument("--max-product-combinations", type=int, default=1_000_000, help="Per-pair inward-facing candidate comparison cap")
    parser.add_argument("--max-blast-rows", type=int, default=1_000_000)
    parser.add_argument("--blastn", default="blastn", help="Local BLAST+ blastn executable")
    parser.add_argument("--makeblastdb", default="makeblastdb", help="Local BLAST+ makeblastdb executable")
    parser.add_argument("--blast-max-target-seqs", type=int, default=100_000)
    parser.add_argument("--blast-max-hsps", type=int, default=100_000)
    parser.add_argument("--blast-timeout", type=int, default=300, help="Seconds per BLAST+ invocation")
    return parser


def run(args):
    settings = {key: value for key, value in vars(args).items()
                if key not in {"pairs", "reference", "expected", "output"}}
    report = {
        "schema_version": "1.0", "analysis": "local_primer_specificity", "status": "incomplete",
        "complete_within_model": False, "exhaustive": False,
        "created_utc": datetime.now(timezone.utc).isoformat(),
        "coordinate_convention": "0-based half-open forward-reference; circular end may exceed record length",
        "strand_convention": "+ matches reference sequence and extends right; - matches its reverse complement and extends left",
        "settings": settings, "provenance": {"python": platform.python_version()},
        "limitations": [
            "Potential amplification under a bounded ungapped mismatch model; not experimental specificity.",
            "Only supplied reference records, mismatch thresholds, 3-prime window and product-size range are screened.",
            "Unrepresented variants, contaminants, insertions/deletions, and chemistry are not screened.",
            "Reference ambiguity is conservatively compatible when a primer base is possible; mismatches are lower bounds.",
            "Only annealing cores are searched. Tails affect final length but tail-created later-cycle binding is not searched.",
            "F/F, R/R and both F/R orientations are enumerated; overlapping binding sites are excluded.",
        ],
        "issues": [], "pairs": [],
    }
    report["limitations"].append(
        "Multiplex mode treats all cross-pair products as unintended; predeclared intentional cross-pair "
        "designs should be assessed as explicit standalone pairs. Same-oligo products remain in original-pair results."
        if args.multiplex else "Cross-pair multiplex products are not screened; enable --multiplex for a panel."
    )
    try:
        input_paths = [args.pairs, args.reference] + ([args.expected] if args.expected else [])
        check_output_paths(input_paths, [args.output])
        for key in ("min_product", "max_product", "three_prime_bases", "max_comparisons", "max_hits",
                    "max_products", "max_product_combinations", "max_blast_rows", "blast_max_target_seqs",
                    "blast_max_hsps", "blast_timeout", "max_panel_combinations"):
            if settings[key] <= 0:
                raise ValueError(f"{key} must be positive")
        if args.min_product > args.max_product:
            raise ValueError("min_product must not exceed max_product")
        if args.max_mismatches < 0 or args.max_three_prime_mismatches < 0:
            raise ValueError("Mismatch thresholds must be nonnegative")
        if args.max_three_prime_mismatches > args.three_prime_bases:
            raise ValueError("3-prime mismatch threshold exceeds its window")
        pairs, references = read_pairs(args.pairs), read_fasta(args.reference)
        circular = set(args.circular)
        if circular - set(references):
            raise ValueError("Unknown circular record IDs: " + ", ".join(sorted(circular - set(references))))
        primers = sorted({pair[key] for pair in pairs for key in ("forward", "reverse")})
        if not primers or not references:
            raise ValueError("Nonempty primer pairs and reference records are required")
        if any(args.three_prime_bases > len(primer) for primer in primers):
            raise ValueError("3-prime window exceeds a primer's annealing-core length")
        if any(set(primer) - set("ACGT") for primer in primers):
            raise ValueError("Only unambiguous ACGT annealing cores are supported")
        report["provenance"].update({
            "implementation_sha256": {
                name: sha256_file(Path(__file__).parent / name)
                for name in ("screen_specificity.py", "_specificity.py", "_common.py")
            },
            "pairs": {"path": str(args.pairs), "sha256": sha256_file(args.pairs)},
            "reference": {"path": str(args.reference), "sha256": sha256_file(args.reference),
                          "record_count": len(references), "total_bases": sum(map(len, references.values())),
                          "record_lengths": {key: len(value) for key, value in references.items()},
                          "ambiguous_base_count": sum(base not in "ACGT" for sequence in references.values() for base in sequence)},
            "expected": {"path": str(args.expected), "sha256": sha256_file(args.expected)} if args.expected else None,
        })
        expected = read_expected(args.expected, pairs, references, circular)
        original_pair_count = len(pairs)
        if args.multiplex:
            pairs = expand_panel_pairs(pairs, args.max_panel_combinations)
        report["provenance"]["panel"] = {
            "enabled": args.multiplex, "original_pair_count": original_pair_count,
            "source_oligo_count": original_pair_count * 2,
            "cross_pair_count": len(pairs) - original_pair_count,
            "unique_annealing_cores_searched": len(primers),
            "generated_pair_ids": [pair["pair_id"] for pair in pairs if pair.get("cross_pair")],
        }
        search = exhaustive_hits if args.engine == "exhaustive" else blast_hits
        if args.engine == "blast":
            report["limitations"].append(
                "BLAST discovery is heuristic, uses exact 7-base seeds and E-value 1000; full-length ungapped "
                "realignment validates only discovered sites. No-hit output never proves absence of binding sites."
            )
        hits, engine_provenance, issues = search(primers, references, circular, settings)
        report["provenance"].update(engine_provenance)
        report["issues"].extend(issues)
        for pair in pairs:
            result = enumerate_products(pair, hits, references, circular, settings, expected.get(pair["pair_id"], []))
            if result["unresolved_ambiguity"]:
                report["issues"].append(
                    f"Pair {pair['pair_id']}: ambiguous reference bases at potential binding sites; "
                    "binding and 3-prime mismatch counts remain unresolved"
                )
            if issues:
                result.setdefault("status_before_incomplete", result["status"])
                result["status"] = "incomplete"
            report["pairs"].append(result)
        report["complete_within_model"] = args.engine == "exhaustive" and not report["issues"]
        report["exhaustive"] = args.engine == "exhaustive" and not report["issues"]
        report["discovery_completed"] = not issues
        statuses = {pair["status"] for pair in report["pairs"]}
        report["status"] = next(status for status in (
            "incomplete", "intended_target_not_found", "potential_off_target", "no_expected_target",
            "no_off_target_found_within_search_scope",
        ) if status in statuses)
        return report, 2 if report["issues"] else 0
    except (ValueError, OSError, KeyError) as exc:
        report["issues"].append(str(exc))
        report["status"] = "incomplete"
        report["complete_within_model"] = False
        report["exhaustive"] = False
        report["discovery_completed"] = False
        for pair in report["pairs"]:
            pair.setdefault("status_before_incomplete", pair["status"])
            pair["status"] = "incomplete"
        return report, 2


def main(argv=None):
    args = build_parser().parse_args(argv)
    # Never replace an input with an error report, either.
    inputs = [args.pairs, args.reference] + ([args.expected] if args.expected else [])
    try:
        check_output_paths(inputs, [args.output])
    except (ValueError, OSError) as exc:
        print(f"error: {exc}", file=sys.stderr)
        return 2
    report, exit_code = run(args)
    try:
        write_json(args.output, report)
    except OSError as exc:
        print(f"Cannot write report: {exc}", file=sys.stderr)
        return 2
    print(f"{report['status']}: {args.output}")
    return exit_code


if __name__ == "__main__":
    raise SystemExit(main())

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

The author says: 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.

Security

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Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/scientific-agent-skills · skills/primer-design · commit a25cf9e
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE.md)
  • ATTRIBUTION.md (written by AutoGineer)
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download primer-design.zip