Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome /…
$ npx skills add GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-clinical-databases-msi-detection --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/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/clinical-databases/msi-detection .claude/skills/bio-clinical-databases-msi-detection && 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 "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .claude/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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/GPTomics/bioSkills/tree/main/clinical-databases/msi-detectionType 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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-clinical-databases-msi-detection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/clinical-databases/msi-detection .agents/skills/bio-clinical-databases-msi-detection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .agents/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-clinical-databases-msi-detection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/clinical-databases/msi-detection .cursor/skills/bio-clinical-databases-msi-detection && 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 "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .cursor/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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/GPTomics/bioSkills.git --path clinical-databases/msi-detection--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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-clinical-databases-msi-detection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/clinical-databases/msi-detection .gemini/skills/bio-clinical-databases-msi-detection && 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 "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .gemini/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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 GPTomics/bioSkills bio-clinical-databases-msi-detectionInstalls 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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/clinical-databases/msi-detection .github/skills/bio-clinical-databases-msi-detection && 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 "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .github/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-clinical-databases-msi-detection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/clinical-databases/msi-detection .opencode/skills/bio-clinical-databases-msi-detection && 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 "bio-clinical-databases-msi-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/clinical-databases/msi-detection into .opencode/skills/bio-clinical-databases-msi-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-clinical-databases-msi-detection", 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.
bio-clinical-databases-msi-detectionCalls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome /…
Bio Clinical Databases Msi Detection is an agent skill from GPTomics/bioSkills. Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker. Use when stratifying ICI eligibility (Le 2015), pairing MSI with TMB-H (Sha 2020 / Salem 2018), screening Lynch syndrome (universal IHC + MSI), or distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.
Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/msi_detection.sh` and `usage-guide.md`).
It sits in Research & Science. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.
Read from SKILL.md and the folder at commit d91ed3d. 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 script files (Shell), which the agent can run.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From 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.
Bio Clinical Databases Msi Detection loads about 5k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 1,805 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); files beside SKILL.md are not scanned.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,805 words, ~4,958 tokens.
.claude/skills/bio-clinical-databases-msi-detection/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Reference examples tested with: MSIsensor-pro 1.2+, MSIsensor 0.6+, MANTIS 1.0.5+, samtools 1.19+, mSINGS 5.6+, pandas 2.2+, cyvcf2 0.30+. FDA pembrolizumab MSI-H / dMMR pan-tumor approval is from 2017 (Le 2015 NEJM; KEYNOTE-016/164/158); approval extended to colorectal first-line in 2020.
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --versionIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. MSIsensor-pro replaces MSIsensor for tumor-only assays; MSIsensor-ct is the bTMB-equivalent for ctDNA panels.
'Detect MSI status from this somatic sequencing data' -> Profile microsatellite instability across canonical loci (Bethesda 5 panel + extended NGS-derived sites); classify MSI-H / MSS / MSI-L per Bethesda / FDA / KEYNOTE convention.
msisensor-pro msi -d microsatellites.list -t tumor.bam -o msi_out -b 16msisensor msi -d microsatellites.list -n normal.bam -t tumor.bam -o msi_outmsisensor-ct ...mantis -t tumor.bam -n normal.bam -b targets.bed --threads 8| Event | Year | Threshold | Notes |
|---|---|---|---|
| Le 2015 NEJM | 2015 | MSI-H + ICI in CRC | The seminal paper: pembrolizumab in MSI-H CRC ORR 40% vs 0% MSS |
| FDA pembrolizumab MSI-H / dMMR pan-tumor | 2017 | MSI-H | First tissue-agnostic FDA approval (KEYNOTE-016/164/158) |
| FDA pembrolizumab first-line MSI-H CRC | 2020 | MSI-H + first-line CRC | KEYNOTE-177 |
| CheckMate 142 | 2017-2018 | MSI-H + nivolumab/ipilimumab | Pan-tumor MSI-H second-line |
| ESMO 2024 | 2024 | MSI-H | Maintained pan-tumor MSI-H biomarker |
| Universal Lynch screening | -- | IHC + MSI on all CRC <= 70 yr | NCCN / ACG / EGAPP guidelines |
| Term | Definition | Method | Relationship |
|---|---|---|---|
| dMMR (deficient MMR) | Loss of MMR protein function | IHC (MLH1, MSH2, MSH6, PMS2) | Causes MSI |
| MSI-H | Microsatellite instability high | PCR-based Bethesda or NGS | Consequence of dMMR |
| Lynch syndrome | Germline MMR mutation | Germline sequencing | Causes ~50% of MSI-H CRC; rest are sporadic (MLH1 hyper-methylation) |
| TMB-H | >= 10 mut/Mb | NGS panel / WES | Statistical correlate of MSI-H |
| POLE-exo hypermutator | POLE proofreading defect | Sequencing / signatures | Hypermutator WITHOUT MMR-D; MSI-stable typically |
MSI-H + TMB-H overlap (Chalmers 2017 Genome Med 9:34):
POLE-exo vs MMR-D:
| Tool | Paired | Tumor-only | ctDNA | Algorithm | Fails when |
|---|---|---|---|---|---|
| MSIsensor (Niu 2014 Bioinformatics) | Yes | No | No | Bayesian + read-length distribution | Tumor-only data (no baseline); cohort baseline missing |
| MSIsensor-pro (Jia 2020 Genom Proteom Bioinform) | Optional | Yes | No | Distribution comparison to baseline | Baseline cohort not provided; panel < 50 loci |
| MSIsensor-ct (Han 2021 Brief Bioinform) | -- | -- | Yes | cfDNA-aware | Tumor fraction < 3%; low ctDNA shed |
| MANTIS (Kautto 2017 Oncotarget) | Yes | No | No | Step-wise difference | Tumor-only; low coverage at microsatellites |
| mSINGS (Salipante 2014 Clin Chem) | -- | Yes | No | Background panel (unstable-loci fraction) | Background panel poorly characterized for cohort |
Operational consensus 2024-2026:
| Scenario | Recommended path | Why |
|---|---|---|
| Tumor + paired normal WES | MSIsensor (standard) | Reference paired-normal comparison |
| Tumor-only WES/panel | MSIsensor-pro with panel baseline | No matched normal needed |
| ctDNA / liquid biopsy | MSIsensor-ct | cfDNA-aware |
| Lynch syndrome screening | Universal IHC + MSI (NCCN) | IHC catches 90%+; MSI for IHC-equivocal |
| FDA pembrolizumab eligibility | Validate per FoCR PCR + IHC + NGS concordance | Cross-platform required |
| MSI-H + TMB-H concurrence | MSI-H is primary biomarker | Sha 2020; TMB-H not additive |
| POLE+MMR ultra-hypermutator | Sigprofiler signatures (SBS14, SBS20) | Mechanism beyond MSI alone |
| Sporadic MSI-H | Confirm MLH1 hypermethylation; rule out Lynch | Distinguishes sporadic vs germline |
| MSI-stable + TMB-H | Investigate POLE-exo signature (SBS10a/10b) | POLE-exo causes hypermutator without MSI |
| Pan-tumor screening | MSI + IHC + TMB combined | Multiple modalities for ICI eligibility |
The original NCI/Bethesda reference panel (Boland 1998) used BAT-25 and BAT-26 plus three dinucleotide markers (D2S123, D5S346, D17S250); >= 2 of 5 loci unstable -> MSI-H. Modern PCR assays use the mononucleotide pentaplex (the current clinical standard), which replaced the dinucleotide markers for improved cross-population specificity:
NGS-based MSI panels use 50-1000+ microsatellite loci. MSI-H requires unstable status at >=40% of tested loci typically (varies by panel calibration).
Goal: Compute MSI status from tumor-only WES/panel.
Approach: Generate baseline from population reference; compare patient tumor.
# Generate microsatellite list from reference genome (one-time)
msisensor-pro scan -d /reference/GRCh38.fa -o microsatellites.list -p 1 -m 5
# Generate baseline from N normal control samples (one-time per panel)
msisensor-pro baseline -d microsatellites.list -i normal_samples.list -o baseline.list -b 16
# Score tumor sample. The `-i sample_id` flag is uncommon: in typical msisensor-pro
# usage the sample identifier is derived from the BAM file -- verify the flag set
# against `msisensor-pro pro --help` for the installed release.
msisensor-pro pro \
-d microsatellites.list \
-t tumor.bam \
-o msi_output \
-b 16 \
--baseline baseline.list
# Output: msi_output_all (raw); msi_output_unstable (unstable loci); msi_output.txt (summary)
# Critical column: %_unstable. Threshold MSI-H typically >= 20-30% depending on panel.msisensor msi \
-d microsatellites.list \
-n normal.bam \
-t tumor.bam \
-o msi_paired_out \
-b 16
# Output: %_unstable in paired comparison
# MSI-H threshold: >= 20% by FoCR guidance; varies 10-30% across studiesmantis.py \
-t tumor.bam \
-n normal.bam \
-b microsatellite_targets.bed \
--threads 8 \
-o mantis_output
# Output: mantis_output.kmer_counts (raw), mantis_output (status)
# Threshold MSI-H: stepwise difference > 0.4 (default)import pandas as pd
def classify_msi(unstable_percentage, panel_calibrated_cutoff=20.0):
'''Classify MSI status from percentage of unstable loci.
Bethesda PCR: >=2 of 5 unstable -> MSI-H (40% loci)
NGS: panel-specific cutoffs typically 10-30%
Concordance: MSI-PCR + IHC + NGS should agree (FoCR)
'''
if unstable_percentage >= panel_calibrated_cutoff:
return 'MSI-H'
elif unstable_percentage >= panel_calibrated_cutoff / 2:
return 'MSI-L (intermediate; treat as MSS clinically per FDA)'
else:
return 'MSS'
def msi_lynch_workflow(msi_status, ihc_results, mlh1_methylation_status, germline_test):
'''Standard Lynch syndrome workflow.
Args:
msi_status: 'MSI-H' / 'MSS' / 'MSI-L'
ihc_results: dict {MLH1: 'retained' or 'loss', MSH2, MSH6, PMS2}
mlh1_methylation_status: 'methylated' (sporadic) / 'unmethylated' (Lynch suspect)
germline_test: 'positive' / 'negative' / 'not_performed'
'''
if msi_status != 'MSI-H':
return 'No further Lynch screening indicated'
ihc_loss = [gene for gene, status in ihc_results.items() if status == 'loss']
if not ihc_loss:
return 'MSI-H with retained IHC; consider Lynch with germline testing'
if 'MLH1' in ihc_loss:
if mlh1_methylation_status == 'methylated':
return 'Sporadic MSI-H (MLH1 hypermethylation); not Lynch'
elif mlh1_methylation_status == 'unmethylated':
return 'Lynch suspect (MLH1 loss without methylation); proceed with germline testing'
else:
return 'MLH1 loss; perform methylation test'
return f'MSH2/6/PMS2 loss ({", ".join(ihc_loss)}); strong Lynch suspect; germline testing'
def msi_tmb_ici_decision(msi_status, tmb_value, tumor_type=None, dmmr_ihc=None):
'''Integrated ICI eligibility from MSI + TMB.
Sha 2020: MSI-H is primary biomarker; TMB-H not additive.
McGrail 2021: TMB-H NOT endorsed for breast/prostate/glioma alone.
'''
msi_high = msi_status == 'MSI-H'
dmmr_positive = dmmr_ihc == 'positive'
tmb_h = tmb_value >= 10
if msi_high or dmmr_positive:
return ('ICI eligible: MSI-H or dMMR (FDA pembrolizumab 2017 pan-tumor; KEYNOTE-016/164/158); '
'TMB-H is not additive (Sha 2020).')
if tmb_h and tumor_type and tumor_type.lower() in ('breast', 'prostate', 'glioma'):
return ('TMB-H but tumor type excluded by ESMO 2024 / McGrail 2021. '
'Consider tumor-type-specific cutoff.')
if tmb_h:
return 'TMB-H pan-tumor (FDA pembrolizumab 2020); ICI eligible.'
return 'MSS + TMB-low. Standard chemo per tumor type.'1. Tumor-only with paired-normal tool
2. Panel size too small
3. IHC vs MSI discordance not investigated
4. MSI-H + Lynch syndrome confusion
5. POLE-exo hypermutator labeled MSI
6. ctDNA MSI without sufficient tumor fraction
7. Universal screening missed
8. MSI-L treated as actionable
| Pattern | Likely cause | Action |
|---|---|---|
| PCR Bethesda MSI-H vs NGS MSS | Bethesda panel uses 5 loci only; less sensitive | Trust NGS with >=50 informative loci |
| NGS MSI-H vs IHC retained | Subtle MMR loss; MSH6-only subtype; or POLE-exo | Confirm with germline + POLE-exo signature analysis |
| Paired-normal MSI-H + tumor-only MSS | Sample swap or low tumor purity in tumor-only | Re-validate; check purity (>=20% required) |
| MSIsensor-pro vs MSIsensor (paired) | Different baseline thresholds | Apply panel-specific calibration |
| MSI-H suspected but tools differ | Borderline mutational burden | Use signature analysis (SBS6/15/26/44) as orthogonal evidence |
| ctDNA MSI vs tissue MSI | Tumor fraction low | Trust tissue; estimate ctDNA fraction |
| Threshold | Convention | Source |
|---|---|---|
| Bethesda MSI-H | >= 2/5 unstable | Boland 1998 |
| NGS MSI-H cutoff | 10-30% unstable loci (panel-specific) | Various |
| MANTIS MSI-H threshold | Step-wise difference > 0.4 | Kautto 2017 |
| MSIsensor MSI-H threshold | >= 20% by FoCR | Friends of Cancer Research |
| Minimum informative loci | >= 50 NGS loci | Panel-design convention |
| ctDNA tumor fraction minimum | >= 3% for reliable cfDNA MSI (depth-dependent operational floor; MSIsensor-ct reports 0.05% LOD only at >= 3000x) | Operational convention |
| Tumor purity minimum | >= 20% | Standard |
| FDA approval | MSI-H or dMMR pan-tumor (2017) | KEYNOTE-016/164/158 |
| First-line MSI-H CRC | KEYNOTE-177 (2020) | -- |
| MSI-H -> TMB-H rate | ~83% | Chalmers 2017 |
| TMB-H -> MSI-H rate | ~16% | Chalmers 2017 |
| Sporadic MSI-H mechanism | ~50% MLH1 hypermethylation | Various |
| Universal screening cutoff | CRC <= 70 yr | NCCN / ACG |
| Symptom | Cause | Solution |
|---|---|---|
| MSI-H + IHC retained discordance | Subtle loss; MSH6-only; or rare hypermutator | Cross-check germline + signatures |
| Borderline MSI call | Panel too small | Use >= 50 informative loci |
| Tumor-only MSI low confidence | Background subtraction needed | Use MSIsensor-pro with cohort baseline |
| MSI-H + TMB-H reported additive | Tautology per Sha 2020 | MSI-H is primary; TMB-H not additive |
| POLE-exo labeled MMR-D | Different mechanism; mutation count differs | Run Sigprofiler; SBS10a/10b is POLE-exo |
| Sporadic MSI-H mis-labeled Lynch | Need MLH1 methylation test | Confirm MLH1 methylation + germline |
| Pushback | Standard response |
|---|---|
| "MSI-H + TMB-H both reported additive" | Sha 2020 Cancer Discov: MSI-H is the primary biomarker; TMB-H is statistical correlate. We report MSI-H first; TMB-H reported but noted not additive. |
| "Why MSIsensor-pro instead of MSIsensor?" | MSIsensor requires paired normal; MSIsensor-pro handles tumor-only via cohort baseline. Most commercial panels are tumor-only. |
| "MSI-PCR vs NGS discordant" | Bethesda 5-locus panel is less sensitive; we use NGS >=50 informative loci for confirmation. |
| "Universal Lynch screening?" | NCCN / ACG recommend reflex IHC + MSI on all CRC <= 70 yr; we implemented universal screening protocol. |
| "POLE-exo hypermutator with MSI-H?" | Sigprofiler signature analysis distinguishes: SBS10a/10b = POLE-exo (typically MSI-stable); SBS6/15/26/44 = MMR-D. POLE+MMR concurrent produces ultra-hypermutator. |
| "MSI-L?" | FDA approval specifies MSI-H; MSI-L = clinically MSS; we apply MSI-H threshold strictly. |
| "ctDNA MSI viability?" | MSIsensor-ct works if tumor fraction >= 3%; we estimate via ichorCNA; below threshold falls back to tissue. |
© GPTomics, 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 2 other files in clinical-databases/msi-detection of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.
Bio Clinical Databases Msi Detection 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 |
|---|---|---|---|---|---|---|
| Bio Clinical Databases Msi Detection this skillGPTomics/bioSkills | 1.2k | 2 repos | ~5k | 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.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome /…. Bio Clinical Databases Msi Detection is an agent skill from GPTomics/bioSkills. Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker.
Bio Clinical Databases Msi Detection fits situations like: stratifying ICI eligibility (Le 2015); pairing MSI with TMB-H (Sha 2020 / Salem 2018); screening Lynch syndrome (universal IHC + MSI); distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.
Run `npx skills add GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a claude-code`. Or copy the skill folder (clinical-databases/msi-detection in GPTomics/bioSkills) into .claude/skills/bio-clinical-databases-msi-detection in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a codex`. Or copy the skill folder (clinical-databases/msi-detection in GPTomics/bioSkills) into .agents/skills/bio-clinical-databases-msi-detection 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 GPTomics/bioSkills --skill bio-clinical-databases-msi-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-clinical-databases-msi-detection, .gemini/skills/bio-clinical-databases-msi-detection, .github/skills/bio-clinical-databases-msi-detection and .opencode/skills/bio-clinical-databases-msi-detection in your project.
Going by SKILL.md and its folder, Bio Clinical Databases Msi Detection needs a shell for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3; A Bash shell.
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. 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. Review the folder before installing.
Bio Clinical Databases Msi Detection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Bio Clinical Databases Msi Detection: 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.
GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.
Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.