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Bio Primer Design Primer Basics · FreedomIntelligence | DecimalAI

Bio Primer Design Primer Basics

SkillScore96Maintained

bio-primer-design-primer-basics

Design PCR primers for a target sequence using primer3-py. Specify target regions, product size, melting temperature, and other constraints. Returns ranked primer pairs with quality metrics. Use when designing standard PCR primers.

scienceby FreedomIntelligence

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Research pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/bio-primer-design-primer-basics/SKILL.md

decimalai skills pull bio-primer-design-primer-basics --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/bio-primer-design-primer-basics/SKILL.md https://app.decimal.ai/s/bio-primer-design-primer-basics/SKILL.md

Raw file: /s/bio-primer-design-primer-basics/SKILL.md ↗ · machine-readable summary at /s/bio-primer-design-primer-basics.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 50d ago

Yes — +30% lift · at higher cost+78% tokens0% turns96% of whole cases pass with skill (22/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	39%	0%
case-02	✗→✓	▲ Improved	120%	0%
case-03	✗→✓	▲ Improved	49%	0%
case-06	✗→✓	▲ Improved	11%	0%
case-07	✗→✓	▲ Improved	82%	0%

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

FreedomIntelligence / OpenClaw-Medical-Skills

skills/bio-primer-design-primer-basics/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v2

Skill contents

SKILL.md

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

Provenance: Authenticated by MD BABU MIA

-->

PCR Primer Design

Design PCR primers using primer3-py, the Python binding for Primer3.

Required Imports

python
import primer3
from primer3 import p3helpers
from Bio import SeqIO
from Bio.Seq import Seq

Sequence Preparation (p3helpers)

python
# Sanitize sequence (uppercase, remove whitespace)
raw_seq = '  atgc gatc GATC  '
clean_seq = p3helpers.sanitize_sequence(raw_seq)
print(f'Cleaned: {clean_seq}')  # 'ATGCGATCGATC'

# Reverse complement for designing reverse primers
seq = 'ATGCGATCGATC'
rc_seq = p3helpers.reverse_complement(seq)
print(f'Reverse complement: {rc_seq}')  # 'GATCGATCGCAT'

# Ensure valid DNA sequence (ACGT only, uppercase)
valid_seq = p3helpers.ensure_acgt_uppercase('atgcNNgatc')  # Raises error if invalid

Basic Primer Design

python
sequence = 'ATGCGTACGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG'

result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]],
        'PRIMER_MIN_TM': 57.0,
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MAX_TM': 63.0,
        'PRIMER_MIN_GC': 40.0,
        'PRIMER_MAX_GC': 60.0,
    }
)

Extract Primer Results

python
num_returned = result['PRIMER_PAIR_NUM_RETURNED']
print(f'Found {num_returned} primer pairs')

for i in range(num_returned):
    left = result[f'PRIMER_LEFT_{i}_SEQUENCE']
    right = result[f'PRIMER_RIGHT_{i}_SEQUENCE']
    left_tm = result[f'PRIMER_LEFT_{i}_TM']
    right_tm = result[f'PRIMER_RIGHT_{i}_TM']
    product_size = result[f'PRIMER_PAIR_{i}_PRODUCT_SIZE']
    print(f'Pair {i}: {left} / {right}')
    print(f'  Tm: {left_tm:.1f}C / {right_tm:.1f}C, Product: {product_size}bp')

Target a Specific Region

python
# Target a specific region: [start, length]
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_TARGET': [100, 50],  # Target region at position 100, length 50
    },
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[150, 300]],
        'PRIMER_OPT_TM': 60.0,
    }
)

Primers Must Span a Region

python
# Primers must span this region (e.g., exon junction)
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_INCLUDED_REGION': [50, 200],  # Primers within this region
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 250]]}
)

Exclude Regions

python
# Exclude regions (e.g., SNP positions, repeats)
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_EXCLUDED_REGION': [[150, 20], [300, 15]],  # Regions to avoid
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]]}
)

Constrain Primer Positions

python
# Force primer to overlap a specific position
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_FORCE_LEFT_START': 50,   # Left primer must start here
        'SEQUENCE_FORCE_RIGHT_START': 250,  # Right primer must start here
    },
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[150, 250]]}
)

Design for Sequencing

python
# Single primer for sequencing
result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence},
    global_args={
        'PRIMER_PICK_LEFT_PRIMER': 1,
        'PRIMER_PICK_RIGHT_PRIMER': 0,  # Only design left primer
        'PRIMER_PICK_INTERNAL_OLIGO': 0,
        'PRIMER_OPT_SIZE': 20,
        'PRIMER_MIN_SIZE': 18,
        'PRIMER_MAX_SIZE': 25,
    }
)

Full Parameter Control

python
result = primer3.design_primers(
    seq_args={
        'SEQUENCE_TEMPLATE': sequence,
        'SEQUENCE_TARGET': [200, 50],
    },
    global_args={
        'PRIMER_PRODUCT_SIZE_RANGE': [[150, 300], [300, 500]],  # Multiple ranges
        'PRIMER_NUM_RETURN': 5,
        'PRIMER_MIN_SIZE': 18,
        'PRIMER_OPT_SIZE': 20,
        'PRIMER_MAX_SIZE': 25,
        'PRIMER_MIN_TM': 57.0,
        'PRIMER_OPT_TM': 60.0,
        'PRIMER_MAX_TM': 63.0,
        'PRIMER_MIN_GC': 40.0,
        'PRIMER_OPT_GC_PERCENT': 50.0,
        'PRIMER_MAX_GC': 60.0,
        'PRIMER_MAX_POLY_X': 4,           # Max consecutive identical bases
        'PRIMER_MAX_NS_ACCEPTED': 0,       # No ambiguous bases
        'PRIMER_MAX_SELF_ANY': 8,          # Self-complementarity
        'PRIMER_MAX_SELF_END': 3,          # 3' self-complementarity
        'PRIMER_PAIR_MAX_COMPL_ANY': 8,    # Pair complementarity
        'PRIMER_PAIR_MAX_COMPL_END': 3,    # Pair 3' complementarity
        'PRIMER_MAX_END_STABILITY': 9.0,   # Max 3' end stability (delta G)
    }
)

Load Sequence from FASTA

python
from Bio import SeqIO

record = SeqIO.read('gene.fasta', 'fasta')
sequence = str(record.seq)

result = primer3.design_primers(
    seq_args={'SEQUENCE_TEMPLATE': sequence, 'SEQUENCE_ID': record.id},
    global_args={'PRIMER_PRODUCT_SIZE_RANGE': [[100, 300]], 'PRIMER_OPT_TM': 60.0}
)

Calculate Tm Directly

python
# Calculate Tm for an existing primer
tm = primer3.calc_tm('ATGCGATCGATCGATCGATC')
print(f'Tm: {tm:.1f}C')

# With custom salt/DNA concentrations
tm = primer3.calc_tm('ATGCGATCGATCGATCGATC', mv_conc=50.0, dv_conc=1.5, dntp_conc=0.2, dna_conc=50.0)

Tm Calculation Defaults

| Parameter | Default | Description | |-----------|---------|-------------| | mv_conc | 50.0 mM | Monovalent cations (Na+, K+) | | dv_conc | 0.0 mM | Divalent cations (Mg2+) | | dntp_conc | 0.0 mM | dNTP concentration | | dna_conc | 50.0 nM | DNA oligo concentration |

Calculate Hairpin and Dimer Tm

python
# Hairpin Tm
hairpin = primer3.calc_hairpin('ATGCGATCGATCGATCGATC')
print(f'Hairpin Tm: {hairpin.tm:.1f}C, dG: {hairpin.dg:.1f}')

# Homodimer Tm
homodimer = primer3.calc_homodimer('ATGCGATCGATCGATCGATC')
print(f'Homodimer Tm: {homodimer.tm:.1f}C, dG: {homodimer.dg:.1f}')

# Heterodimer Tm (between two different primers)
heterodimer = primer3.calc_heterodimer('ATGCGATCGATCGATCGATC', 'GCTAGCTAGCTAGCTAGCTA')
print(f'Heterodimer Tm: {heterodimer.tm:.1f}C, dG: {heterodimer.dg:.1f}')

Format Results as DataFrame

python
import pandas as pd

def primers_to_dataframe(result):
    rows = []
    for i in range(result['PRIMER_PAIR_NUM_RETURNED']):
        rows.append({
            'pair': i,
            'left_seq': result[f'PRIMER_LEFT_{i}_SEQUENCE'],
            'right_seq': result[f'PRIMER_RIGHT_{i}_SEQUENCE'],
            'left_tm': result[f'PRIMER_LEFT_{i}_TM'],
            'right_tm': result[f'PRIMER_RIGHT_{i}_TM'],
            'left_gc': result[f'PRIMER_LEFT_{i}_GC_PERCENT'],
            'right_gc': result[f'PRIMER_RIGHT_{i}_GC_PERCENT'],
            'product_size': result[f'PRIMER_PAIR_{i}_PRODUCT_SIZE'],
            'penalty': result[f'PRIMER_PAIR_{i}_PENALTY'],
        })
    return pd.DataFrame(rows)

df = primers_to_dataframe(result)
print(df)

Common Global Arguments

| Parameter | Description | Default | |-----------|-------------|---------| | PRIMER_PRODUCT_SIZE_RANGE | Allowed product sizes | 100,300]] | | PRIMER_NUM_RETURN | Number of primer pairs | 5 | | PRIMER_MIN/OPT/MAX_SIZE | Primer length | 18/20/27 | | PRIMER_MIN/OPT/MAX_TM | Melting temperature | 57/60/63 | | PRIMER_MIN/MAX_GC | GC content percent | 20/80 | | PRIMER_MAX_POLY_X | Max poly-X run | 5 | | PRIMER_MAX_SELF_ANY | Self complementarity | 8 | | PRIMER_MAX_SELF_END | 3' self complementarity | 3 |

Related Skills

qpcr-primers - Design primers with internal probes for qPCR
primer-validation - Check primers for specificity and secondary structures
sequence-io - Load template sequences
database-access/local-blast - BLAST primers for specificity checking

Illustrative Want to see what bio-primer-design-primer-basics would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

8/2/2026, 16:48 UTC · 23 cases

Lift

+30%

96% vs 65%

Agent turns

1.0 vs 1.0

Tokens

+78%

4,297.57 vs 2,408.61

fail → pass · 8pass → pass · 14pass → fail · 1

Per-case results (23)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-01 I need a Python script using primer3-py to design standard PCR primers for a sequence string. The workflow should first clean up the input DNA sequence to ensure uppercase ACGT characters, set up primer design around a specific 50 bp target region, and extract the resulting primer pairs into a pandas DataFrame showing left and right primer sequences, melting temperatures, GC percentages, and amplicon lengths.	fail→pass	15,795	9,170	-42%	1	1	0%	3,561	4,945	+39%	0	0	—
case-18 Write a Python snippet to set maximum pair complementarity limits during primer selection: max pair overall complementarity of 8 and max pair 3' end complementarity of 3.	pass→pass	6,112	2,820	-54%	1	1	0%	1,186	3,457	+191%	0	0	—
case-02 Write a Python function to take an uncleaned DNA sequence string containing lowercase characters and spaces, clean it up, validate that it contains only valid uppercase ACGT characters, and return both the cleaned string and its reverse complement.	fail→pass	9,443	6,397	-32%	1	1	0%	1,877	4,127	+120%	0	0	—
case-03 Write a Python script to design PCR primers spanning a specific 40 bp mutation region starting at base position 120 in a template DNA sequence. The template sequence is provided as a variable `seq`.	fail→pass	13,776	7,279	-47%	1	1	0%	2,986	4,443	+49%	0	0	—
case-04 Write a Python snippet to configure primer design parameters so that all designed primers fall strictly within the region from position 50 to position 250 of a template sequence.	pass→pass	7,233	5,980	-17%	1	1	0%	1,405	4,123	+193%	0	0	—
case-05 Write a Python code snippet to design PCR primers on a DNA sequence while explicitly avoiding two problematic SNP regions: one 20 bp region starting at offset 150, and one 15 bp region starting at offset 300.	pass→pass	11,559	5,835	-50%	1	1	0%	2,404	4,050	+68%	0	0	—
case-06 Write Python code to design a PCR primer pair where the forward primer is constrained to start exactly at index 50 and the reverse primer is constrained to start exactly at index 250.	fail→pass	18,404	6,692	-64%	1	1	0%	3,914	4,356	+11%	0	0	—
case-07 Write a Python snippet to pick only a single forward primer for Sanger sequencing across a target sequence, disabling reverse primer and internal probe generation.	fail→pass	12,081	6,710	-44%	1	1	0%	2,423	4,411	+82%	0	0	—
case-08 Write Python code to configure primer design parameters requiring optimal Tm of 60.0°C (range 57-63°C), GC content between 40% and 60%, and prohibiting single-nucleotide repeats (homopolymeric runs) longer than 4 consecutive identical bases.	pass→pass	11,992	5,383	-55%	1	1	0%	2,617	4,114	+57%	0	0	—
case-09 Write Python code to configure thermodynamic quality thresholds for primer design: maximum self-complementarity of 8, maximum 3' self-complementarity of 3, and maximum 3' end stability (delta G) of 9.0.	pass→pass	7,694	5,519	-28%	1	1	0%	1,672	4,081	+144%	0	0	—
case-10 Write a Python snippet to design primers that can produce amplicons in either a short range (150 to 300 bp) or a long range (300 to 500 bp), returning up to 5 candidate pairs.	fail→pass	10,446	5,133	-51%	1	1	0%	2,418	4,026	+67%	0	0	—
case-11 Write a Python snippet to calculate the melting temperature of primer 'ATGCGATCGATCGATCGATC' under custom reaction conditions: 50.0 mM monovalent cations, 1.5 mM divalent cations, 0.2 mM dNTPs, and 50.0 nM DNA concentration.	fail→pass	7,300	2,802	-62%	1	1	0%	1,406	3,505	+149%	0	0	—
case-12 Write Python code to evaluate potential secondary structure issues for an oligonucleotide 'ATGCGATCGATCGATCGATC' by calculating its hairpin melting temperature and homodimer formation melting temperature and free energy.	pass→pass	16,058	4,501	-72%	1	1	0%	3,354	3,807	+14%	0	0	—
case-13 Write Python code to compute the cross-dimerization (heterodimer) melting temperature and delta G between forward primer 'ATGCGATCGATCGATCGATC' and reverse primer 'GCTAGCTAGCTAGCTAGCTA'.	pass→pass	10,460	4,199	-60%	1	1	0%	2,200	3,495	+59%	0	0	—
case-14 Write a Python script that reads a FASTA file named 'gene.fasta' using Biopython, extracts the sequence and sequence ID, and passes them to the primer design tool to search for products between 100 and 300 bp.	fail→pass	10,581	5,181	-51%	1	1	0%	2,344	4,038	+72%	0	0	—
case-15 Write a Python function that takes a raw output dictionary from primer pair design and builds a pandas DataFrame containing columns for pair index, left sequence, right sequence, left Tm, right Tm, left GC, right GC, product size, and pair penalty score.	pass→pass	8,622	5,283	-39%	1	1	0%	1,951	4,055	+108%	0	0	—
case-16 Write a Python snippet to design primers on a sequence prohibiting any ambiguous bases (N's) in the accepted primer sequences.	pass→pass	17,944	4,430	-75%	1	1	0%	3,782	3,763	-1%	0	0	—
case-17 Write Python code to specify primer length preferences: minimum size of 18 bp, optimal size of 20 bp, and maximum size of 25 bp.	pass→pass	6,260	3,670	-41%	1	1	0%	1,253	3,679	+194%	0	0	—
case-19 Write a Python code snippet to configure optimal GC percentage for primer design to 50.0%, with allowed GC range between 40.0% and 60.0%.	pass→pass	6,036	5,662	-6%	1	1	0%	1,186	4,148	+250%	0	0	—
case-20 Write a Python script to create a local BLAST database from human genome FASTA files and run command-line `blastn` with custom word size to check for off-target primer binding sites across the genome.	pass→pass	17,795	13,672	-23%	1	1	0%	3,719	5,940	+60%	0	0	—
case-21 Write a Python script to select dual-labeled TaqMan probes with FAM fluorophore and IBRQ quencher for quantitative RT-PCR, including probe Tm calculation +10°C above primer Tm.	pass→pass	19,020	14,493	-24%	1	1	0%	3,522	5,861	+66%	0	0	—
case-22 Explain how to choose between oligo(dT) primers, random hexamers, and gene-specific primers for cDNA synthesis in an RNA reverse transcription reaction.	pass→fail	12,960	17,529	+35%	1	1	0%	2,418	5,982	+147%	0	0	—
case-23 Write a Python snippet using Biopython to align multiple homologous DNA sequences using ClustalOmega or MUSCLE command-line wrappers.	pass→pass	8,398	8,954	+7%	1	1	0%	1,800	4,438	+147%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +30 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 65.2%
Pass rate with the skill: 95.7%
Cases attempted: 23
Cases counted toward lift: 23
Cases that regressed: 1
Skill version measured: v2
Run completed: 8/2/2026, 16:48 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-01

Prompt

I need a Python script using primer3-py to design standard PCR primers for a sequence string. The workflow should first clean up the input DNA sequence to ensure uppercase ACGT characters, set up primer design around a specific 50 bp target region, and extract the resulting primer pairs into a pandas DataFrame showing left and right primer sequences, melting temperatures, GC percentages, and amplicon lengths.

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run96

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+84% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 49 days ago

Model	Method	Date	Lift
gemini-3.6-flash	verified	7/24/2026	+32%

Other measured skills in the registry, with their headline benchmark lift.