TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked.
$ npx skills add GPTomics/bioSkills --skill bio-hi-c-analysis-contact-pairs -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-hi-c-analysis-contact-pairs --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/hi-c-analysis/contact-pairs .claude/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .claude/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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/hi-c-analysis/contact-pairsType 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-hi-c-analysis-contact-pairs -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-hi-c-analysis-contact-pairs --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/hi-c-analysis/contact-pairs .agents/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .agents/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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-hi-c-analysis-contact-pairs -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-hi-c-analysis-contact-pairs --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/hi-c-analysis/contact-pairs .cursor/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .cursor/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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 hi-c-analysis/contact-pairs--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-hi-c-analysis-contact-pairs -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-hi-c-analysis-contact-pairs --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/hi-c-analysis/contact-pairs .gemini/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .gemini/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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-hi-c-analysis-contact-pairsInstalls 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-hi-c-analysis-contact-pairs -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/hi-c-analysis/contact-pairs .github/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .github/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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-hi-c-analysis-contact-pairs -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-hi-c-analysis-contact-pairs --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/hi-c-analysis/contact-pairs .opencode/skills/bio-hi-c-analysis-contact-pairs && 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-hi-c-analysis-contact-pairs" agent skill from https://github.com/GPTomics/bioSkills/tree/main/hi-c-analysis/contact-pairs into .opencode/skills/bio-hi-c-analysis-contact-pairs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-hi-c-analysis-contact-pairs", 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-hi-c-analysis-contact-pairsTurns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked.
Bio Hi C Analysis Contact Pairs is an agent skill from GPTomics/bioSkills. Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Covers the bwa mem -SP5M / bwa-mem2 / chromap --preset hic alignment idiom (mates mapped as independent single-end reads), pairtools parse vs parse2 and the walks-policy choice (5unique pairwise vs all for Pore-C/Micro-C concatemers), pair-type classification (keep UU and rescued UC), dedup (PCR vs optical/by-tile), select by pairtype/MAPQ/distance, restriction-fragment handling (restrict…
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/fastq_to_pairs.sh` and `usage-guide.md`).
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.
3 steps, taken from the first numbered list in SKILL.md.
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.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
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 Hi C Analysis Contact Pairs loads about 4.7k tokens when it runs. Until then it costs about 264 tokens; SKILL.md has 2,031 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). 2,031 words, ~4,748 tokens.
.claude/skills/bio-hi-c-analysis-contact-pairs/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: pairtools 1.1+, bwa 0.7.17+ (or bwa-mem2 2.2+), chromap 0.2+, samtools 1.19+, cooler 0.10+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagspairtools defaults have shifted across releases (e.g. parse --max-molecule-size is 750 bp in 1.1.x; dedup --backend defaults to scipy). parse and parse2 report DIFFERENT positions by default - confirm --report-position before mixing outputs. If a command errors, introspect with pairtools <subcommand> --help and adapt rather than retrying.
"Turn my Hi-C reads into clean contacts and tell me if the library worked" -> Align mates independently through ligation junctions, classify and deduplicate pairs to a 5'-canonical .pairs file, then read the cis/trans and orientation statistics to decide library quality before any matrix is built.
bwa mem -SP5M ref.fa R1.fq R2.fq | pairtools parse -c chrom.sizes | pairtools sort | pairtools dedup | pairtools statsA Hi-C library's usable signal is not "reads sequenced" - it is the uniquely-mapped, deduplicated, long-range cis contacts. Everything between FASTQ and the matrix exists to strip a specific artifact of proximity-ligation chemistry, and the diagnostic ratios from pairtools stats reveal whether the experiment succeeded before any compute is spent binning it. Three load-bearing consequences:
% long-range cis is the one-number quality metric; trans is the noise floor. True crosslink-ligation contacts are overwhelmingly cis and distance-decaying. Random ligation between two unrelated molecules in solution is as likely to be trans as cis-far, so trans% is a direct readout of the spurious-ligation floor. A good in-situ human library runs cis>=1kb ~50-65%, inter-chromosomal <10%. But these numbers are genome-size dependent - a yeast or bacterial genome legitimately has higher expected trans (more inter-chromosomal volume per cis distance). Never apply a human trans threshold to a microbe.
The ligation junction lives INSIDE the read, so a local/split aligner aligning mates independently is required. A single read often sequences through a ligation junction (locus A | locus B within one read). An end-to-end aligner soft-clips or mis-maps it and the contact is lost. bwa mem -SP5M aligns R1 and R2 as independent single-end reads (-SP skips mate rescue and pairing - proper-pair logic would destroy every long-range and trans contact) and marks the 5'-most chimeric segment primary (-5, the anchor for pairtools' 5' convention). The chimera fraction rises with read length, so on 150bp PE and on Micro-C long reads this is a large, real chunk of contacts.
Keep UU AND rescued UC; selecting only UU silently discards every rescued ligation. A naive select pair_type=="UU" throws away the chimeric reads pairtools successfully reconstructed (UC = combined-unique) - a meaningful fraction on long reads. The 4DN standard keeps UU and UC.
| Aligner | Role | Hi-C invocation | When |
|---|---|---|---|
| bwa mem -SP5M | reference standard; local/split alignment reconstructs in-read junctions | bwa mem -SP5M -t N ref.fa R1 R2 | default; best inter-contig accuracy in benchmarks |
| bwa-mem2 | drop-in faster reimplementation, identical output, same flags | bwa-mem2 mem -SP5M -t N ref.fa R1 R2 | when speed matters and the larger index fits RAM |
| chromap --preset hic | ultrafast integrated align + dedup + pairs (4DN .pairs out) | chromap --preset hic -x idx -r ref.fa -1 R1 -2 R2 -o out.pairs | ~10x faster scans; trades fine walk-policy control for speed |
-SP5M, letter by letter: -S skip mate rescue; -P skip pairing (no proper-pair rescue); together -SP align mates as independent single-end reads. -5 mark the 5'-most split segment primary (anchors the 5' convention). -M is legacy compatibility only (secondary flag 256 vs supplementary 2048); pairtools handles either - never agonize over -M, never drop -SP5.
| Scenario | Recommended | Why |
|---|---|---|
| Standard in-situ/Omni-C, FASTQ -> matrix | bwa mem -SP5M -> parse (5unique) -> sort -> dedup -> stats | the 4DN/distiller default; restriction-agnostic |
| Fastest scan, control not critical | chromap --preset hic | integrated align+dedup+pairs ~10x faster |
| Multi-way contacts (Pore-C, MC-3C, Micro-C walks) | parse --walks-policy all or parse2 --expand | 5unique COLLAPSES concatemers to pairwise silently |
| Micro-C / DNase Hi-C | NO fragment map; do NOT apply a 1kb min-distance cut | sub-1kb (nucleosome ladder) is signal, not artifact |
| Arima / Hi-C 3.0 dual-enzyme, fragment-level | fragment file must encode BOTH motifs (restrict -f) | a single-enzyme digest file is silently wrong |
| Repeat-heavy genome / stringent loop anchors | raise parse --min-mapq 30 | reclassifies borderline reads M, drops repeat false anchors |
| Allele-specific / diploid folding | diploid ref + -XA suboptimal hits -> pairtools phase -> two coolers | needs the suboptimal-score gap to resolve haplotypes |
| Decide whether to sequence deeper | complexity curve from dup model / preseq lc_extrap | a bare dup% without depth is meaningless |
| Build the matrix from clean pairs | -> hic-data-io (cooler cload pairs) | binning happens after classification/dedup |
| Annotate boundary/anchor coordinates | -> genome-intervals/bed-file-basics | pairs are 1-based, half-open conventions differ |
bwa index ref.fa # or bwa-mem2 index ref.fa
bwa mem -SP5M -t 16 ref.fa R1.fq.gz R2.fq.gz | \ # -SP: independent SE; -5: 5' segment primary
samtools view -b -@ 8 - > aligned.bam
# chromap fast path (integrated align + dedup + 4DN pairs, no separate pairtools needed):
# chromap -i -r ref.fa -o idx && \
# chromap --preset hic -x idx -r ref.fa -1 R1.fq.gz -2 R2.fq.gz -o sample.pairs# Parse alignments into a 5'-canonical .pairs. min-mapq 1 (default) is permissive: only MAPQ-0 is "multi".
pairtools parse -c chrom.sizes --walks-policy 5unique --min-mapq 1 \
--add-columns mapq --drop-sam aligned.bam | \
pairtools sort --nproc 8 | \ # block-sort; flips to upper-triangular (5'-canonical)
pairtools dedup --max-mismatch 3 --mark-dups \ # within 3bp on both sides = duplicate; tag DD
--output-stats sample.dedup.stats | \
pairtools select '(pair_type=="UU") or (pair_type=="UC")' \ # keep both-unique AND rescued chimeric
-o sample.valid.pairs.gzDedup MUST run on a flipped, 5'-canonical file (sort does the flip); on a non-canonical file dedup under-collapses and dup% reads falsely low. --max-molecule-size (750 bp in 1.1.x) governs single-ligation chimera rescue; --max-inter-align-gap (20 bp) sets when a coverage gap becomes a null alignment.
pairtools stats is the canonical readout. Read it as a funnel, not a single number.
pairtools stats --bytile-dups -o sample.stats.tsv sample.valid.pairs.gz
# Key fields: frac_dups; frac_cis; cis_1kb+/cis_20kb+; trans; pair_types; dist_freq orientation FF/FR/RF/RR.--bytile-dups separates OPTICAL dups (patterned NovaSeq flowcells, same tile, adjacent coordinates) from PCR dups. Only the PCR fraction reflects library complexity; reading total dup% as over-amplification wrongly condemns a good library. A bare dup% without the depth it was measured at is meaningless - use the complexity/yield curve (preseq lc_extrap) to decide whether deeper sequencing buys uniques or duplicates.Apply the QC-derived distance cut without a fragment map:
pairtools select '(chrom1!=chrom2) or (abs(pos2-pos1) > 1000)' \ # keep trans + cis beyond the orientation-equalization distance
-o sample.filtered.pairs.gz sample.valid.pairs.gzModern pipelines SKIP fragment filtering on purpose - the distance cut + dedup + UU/UC filter + balancing absorb the residual, and a digest file is one genome-specific place to mis-specify the enzyme. pairtools restrict -f frags.bed is opt-in for: sub-kb / restriction-fragment-resolution maps, capture-Hi-C / 4C-style fragment analysis, and bench QC where the dangling-end fraction is the digestion-efficiency readout.
cooler digest --out frags.bed chrom.sizes ref.fa DpnII # single-enzyme: DpnII ^GATC
pairtools restrict -f frags.bed -o restricted.pairs.gz parsed.pairs.gzArima dual-enzyme has FOUR junction motifs (GATCGATC, GANTGATC, GANTANTC, GATCANTC); a single-enzyme (DpnII-only) digest file silently mis-assigns fragments. Micro-C / DNase Hi-C have NO fragment map (MNase/DNase cut sequence-nonspecifically) - any tool requiring a restriction file cannot process them, which is exactly why the restriction-agnostic pairtools path became the field default.
Goal: Resolve each contact to maternal vs paternal haplotype for allele-specific 3D folding.
Approach: Align to a diploid reference reporting suboptimal hits, so each read carries its best alignment on both homologs; pairtools phase reads the gap between the two best alignment scores to tag each side resolved-hap1 / resolved-hap2 / non-resolved / multi-mapper - the score gap is what separates a genuinely uninformative read from an actual repeat.
bcftools consensus -H 1 -f ref.fa phased.vcf.gz > hap1.fa # build a diploid (two-homolog) reference
bcftools consensus -H 2 -f ref.fa phased.vcf.gz > hap2.fa
bwa mem -SP5M ref_diploid.fa R1.fq R2.fq | \ # report suboptimal hits so both homologs are kept
pairtools parse -c chrom.sizes --add-columns XB,AS,XS | \
pairtools phase --phase-suffixes _hap1 _hap2 --tag-mode XB | \
pairtools sort | pairtools dedup -o phased.pairs.gz
# Then split into hap1/hap2/trans pairs and cload each into a SEPARATE cooler.Trigger: plain bwa mem without -SP, or proper-pair logic. Mechanism: mate rescue forces the shotgun insert model on mates from different loci. Symptom: trans% collapses, compartments vanish, sparse map. Fix: bwa mem -SP5M, mates aligned independently.
-5Trigger: copying a pre-2016 -SP command lacking -5. Mechanism: the 5'-most chimeric segment is not primary, so pairtools picks an inconsistent anchor. Symptom: degraded flip/dedup, smeared loops. Fix: always -SP5M (or -SP5).
Trigger: Pore-C/MC-3C/Micro-C walks parsed with the default. Mechanism: 5unique reports only the two 5'-most alignments, collapsing concatemers to pairwise. Symptom: "we found few multi-contacts." Fix: --walks-policy all or parse2 --expand.
Trigger: combining a parse2 (.pairs, default outer/junction-anchored) file with a parse (5'-anchored) file. Mechanism: the two report positions by different conventions. Symptom: coordinates shift by the alignment length; dedup under-collapses; loops smear. Fix: one parser/convention per project; prefer plain parse if walks are not needed.
Trigger: select pair_type=="UU". Mechanism: discards rescued chimeric (UC) pairs. Symptom: lower valid-pair yield, especially on long reads. Fix: keep (pair_type=="UU") or (pair_type=="UC").
Trigger: dedup run before sort/flip, or on mixed parse/parse2 positions. Mechanism: duplicates are not in canonical coordinates. Symptom: dup% reads falsely low - library looks better than it is. Fix: sort (flips to 5'-canonical) before dedup.
Trigger: total dup% on a patterned flowcell taken as library complexity. Mechanism: optical (same-tile) dups inflate apparent PCR rate. Symptom: a good library condemned as over-amplified. Fix: --bytile-dups / --output-bytile-stats; judge complexity on the PCR fraction only.
Trigger: applying Hi-C's >1kb min-distance cut to Micro-C. Mechanism: Micro-C's nucleosome-ladder signal lives below 1kb. Symptom: the structure Micro-C exists to capture is erased. Fix: derive the cut from the orientation-vs-distance plot per library.
| Threshold | Source | Rationale |
|---|---|---|
| cis>=1kb ~50-65% of nodup pairs (good in-situ human) | Dovetail/Arima QC guidance (~approx) | long-range cis is the signal; library- and genome-size dependent |
| inter-chromosomal (trans) <10% (clean), 20-30% acceptable | in-situ Hi-C practice | trans is the spurious-ligation floor; NEVER apply to small genomes |
| FF/FR/RF/RR -> ~25% each above ~1kb | random strand combination at true contacts | convergence is the fragment-map-free positive QC signal |
parse --min-mapq 1 default, raise to 30 for stringency | pairtools default | 1 drops only MAPQ-0; 30 removes repeat-driven false anchors |
dedup --max-mismatch 3 bp | pairtools default | tolerates mapping wobble; 0 over-splits, larger over-collapses complexity |
parse --max-molecule-size 750 bp (1.1.x) | pairtools default | bound on single-ligation chimera rescue; revisit for unusual size selection |
| min-distance cut ~1kb (Hi-C), derive from orientation plot | orientation-equalization distance | the cut is protocol-specific; Micro-C signal is sub-1kb |
| Error / symptom | Cause | Solution |
|---|---|---|
| trans% high, no compartments | aligned without -SP (proper-pair logic) | re-align bwa mem -SP5M |
| Few multi-way contacts on Pore-C/Micro-C | default --walks-policy 5unique collapsed walks | --walks-policy all / parse2 --expand |
| Loops smeared, dedup under-collapses | mixed parse/parse2 position conventions | one parser per project; sort before dedup |
| Valid-pair yield lower than expected | select kept only UU | keep UU and UC |
| dup% suspiciously low | dedup ran before sort/flip | sort to 5'-canonical first |
| dup% high on NovaSeq, complexity looks bad | optical dups counted as PCR | --bytile-dups; judge on PCR fraction |
| Micro-C structure disappears after filtering | fixed 1kb min-distance cut | derive cut from orientation-vs-distance |
| Fragment assignment wrong on Arima data | single-enzyme digest file | encode all four Arima junction motifs |
phase resolves nothing | aligned to a haploid reference | diploid ref + suboptimal (-XA) alignments |
-SP5M idiom© 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 hi-c-analysis/contact-pairs of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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 GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.
Bio Hi C Analysis Contact Pairs 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 Hi C Analysis Contact Pairs this skillGPTomics/bioSkills | 1.2k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.7k | 3 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Pensieve Searcharkohut/pensieve | 1.4k | — | ~8.2k | Automated safety check: Pass | Apache-2.0 |
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
zLanqing/codex-claude-academic-skills
Statistical models library for Python. An agent skill from zLanqing/codex-claude-academic-skills.
zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
timescale/pg-aiguide
A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.
arkohut/pensieve
Search the user's local Pensieve screenshot archive by text, app, or time range.
ninehills/skills
Market prediction skill using Kronos. An agent skill from ninehills/skills.
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
Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Bio Hi C Analysis Contact Pairs is an agent skill from GPTomics/bioSkills.pairs file with pairtools and decides whether the library worked.
Bio Hi C Analysis Contact Pairs fits situations like: processing Hi-C/Micro-C/Omni-C reads into pairs; judging library quality; handling multi-enzyme; restriction-agnostic protocols.
Run `npx skills add GPTomics/bioSkills --skill bio-hi-c-analysis-contact-pairs -a claude-code`. Or copy the skill folder (hi-c-analysis/contact-pairs in GPTomics/bioSkills) into .claude/skills/bio-hi-c-analysis-contact-pairs in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-hi-c-analysis-contact-pairs -a codex`. Or copy the skill folder (hi-c-analysis/contact-pairs in GPTomics/bioSkills) into .agents/skills/bio-hi-c-analysis-contact-pairs 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-hi-c-analysis-contact-pairs -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-hi-c-analysis-contact-pairs, .gemini/skills/bio-hi-c-analysis-contact-pairs, .github/skills/bio-hi-c-analysis-contact-pairs and .opencode/skills/bio-hi-c-analysis-contact-pairs in your project.
Going by SKILL.md and its folder, Bio Hi C Analysis Contact Pairs needs a shell for the scripts in its folder. Our summary lists: A Bash shell.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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 Hi C Analysis Contact Pairs is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k 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 Hi C Analysis Contact Pairs: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 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.