[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QFFTCST979GJT4S451G35J":3,"skill-same:01M6QFFTCST979GJT4S451G35J":116},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":61,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6QFFTCST979GJT4S451G35J","pathogen-variant-surveillance",null,"Query live pathogen genomic surveillance data through the GenSpectrum LAPIS API to find which viral lineages are circulating now, how fast they are growing, and what mutations they carry. Use whenever a question depends on the current state of a pathogen population rather than on remembered facts - which SARS-CoV-2 variant is dominant, whether a Pango lineage is still designated or has been withdrawn, what clade or genotype of H5N1 is in a host or region, whether a PCR primer or assay target still matches circulating sequence, or how a lineage's prevalence has moved week to week. Triggers include \"variant surveillance\", \"genomic surveillance\", \"what variant is circulating\", \"dominant variant\", \"Pango lineage\", \"lineage prevalence\", \"growth advantage\", \"SARS-CoV-2 variant\", \"XFG\", \"clade 2.3.4.4b\", \"H5N1 genotype\", \"influenza clade\", \"RSV\u002Fmpox\u002Fmeasles\u002Fdengue lineage\", \"CoV-Spectrum\", \"LAPIS\", \"Nextclade\", \"pango-designation\", and any request to report what a pathogen population looks like today.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"variant","pathogen variant surveillance","live","for","which","rsv","influenza","epidemiology","host","triggers","python","instances","use","edit","write","read","question","how","PCR","library","2026-11-10 10:12:30",false,"---\nname: pathogen-variant-surveillance\ndescription: Query live pathogen genomic surveillance data through the GenSpectrum LAPIS API to find which viral lineages are circulating now, how fast they are growing, and what mutations they carry. Use whenever a question depends on the current state of a pathogen population rather than on remembered facts - which SARS-CoV-2 variant is dominant, whether a Pango lineage is still designated or has been withdrawn, what clade or genotype of H5N1 is in a host or region, whether a PCR primer or assay target still matches circulating sequence, or how a lineage's prevalence has moved week to week. Triggers include \"variant surveillance\", \"genomic surveillance\", \"what variant is circulating\", \"dominant variant\", \"Pango lineage\", \"lineage prevalence\", \"growth advantage\", \"SARS-CoV-2 variant\", \"XFG\", \"clade 2.3.4.4b\", \"H5N1 genotype\", \"influenza clade\", \"RSV\u002Fmpox\u002Fmeasles\u002Fdengue lineage\", \"CoV-Spectrum\", \"LAPIS\", \"Nextclade\", \"pango-designation\", and any request to report what a pathogen population looks like today.\nlicense: MIT\ncompatibility: Requires Python 3.11+. Scripts use only the standard library - no third-party packages. Needs network access to the public GenSpectrum LAPIS instances (lapis.cov-spectrum.org, lapis.genspectrum.org, lapis.pathoplexus.org) and to raw.githubusercontent.com for pango-designation. No API key.\nallowed-tools: Read Write Edit Bash\nmetadata:\n  version: '1.1'\n  category: life-sciences\n  maintainer: Kalaris Labs\n  last-reviewed: '2026-07-27'\n  tags: pathogen genomics, genomic surveillance, viral lineages, epidemiology\n---\n\n# Pathogen Variant Surveillance\n\n## When to use\n\nAny time an answer depends on what a pathogen population looks like **now**: which lineages are\ncirculating, whether one is growing, what a lineage name currently means, or whether an assay\ntarget still matches.\n\n## The rule\n\n**Never state what is circulating, and never write a lineage name, from memory.**\n\nThree things go wrong at once, and only the first is an ordinary knowledge-cutoff problem:\n\n1. **Names post-date training.** The Pango designation list carries over 6,200 names and grows\n   continuously.\n2. **The nomenclature is a live data structure, not a convention.** `XFG` is a recombinant that\n   only resolves through `alias_key.json`; `PQ.17` unaliases to `XDV.1.5.1.1.8.1.17`. Neither\n   expansion is derivable by reasoning — the mapping is a file that changes.\n3. **Prior knowledge gets retracted, not just outdated.** 294 names in the current\n   `lineage_notes.txt` are withdrawn or redesignated. `PC.2` is now `LF.7.9`; `XFG.20` was\n   withdrawn outright. A remembered lineage fact is not merely stale, it can be actively wrong.\n\nEvery number this skill reports is a count returned by a live instance, stamped with the data\nversion it came from.\n\n## Scope\n\nSurveillance data analysis for research. This skill describes sequences that were collected and\nsubmitted; it does not produce clinical interpretations, outbreak-response recommendations, or\npublic-health guidance, and sequence counts are not case counts.\n\n## Instances\n\nOne API shape covers every pathogen. `--instance` names a verified deployment; `--base-url`\nreaches any other LAPIS instance.\n\n| Instance | Host | Lineage column | Indexed |\n| --- | --- | --- | --- |\n| `sars-cov-2` | lapis.cov-spectrum.org (open GenBank data) | `pangoLineage` | yes |\n| `h5n1`, `h3n2`, `h1n1pdm`, `influenza-a` | lapis.genspectrum.org | `clade` | no |\n| `rsv-a`, `rsv-b`, `mpox`, `measles`, `dengue`, `west-nile`, `hmpv`, `ebola-zaire`, `ebola-sudan`, `cchf` | lapis.pathoplexus.org | varies | varies |\n\n**Field names differ per instance and are never assumed.** Every script reads\n`\u002Fsample\u002FdatabaseConfig` at run time and picks the collection-date, submission-date and lineage\ncolumns from what the instance actually declares. `dateFrom=` is correct on SARS-CoV-2 and a hard\n400 on H5N1, whose collection date is `sampleCollectionDateRangeLower`.\n\n## Scripts\n\n```bash\ncd skills\u002Fpathogen-variant-surveillance\u002Fscripts\n```\n\n| Script | Question answered |\n| --- | --- |\n| `resolve_lineage.py` | Does this name still exist, what does it expand to, what is it descended from? |\n| `lineage_prevalence.py` | What share of sequences is this lineage, week by week, and is it growing? |\n| `mutation_profile.py` | What mutations does it carry, and how does it differ from another lineage? |\n| `reporting_lag.py` | How far back does the data have to go before it can be trusted? |\n\nAll four take `--format table|tsv|json` and print provenance (instance, data version, resolved\nfield names, filters) to stderr, so `> out.tsv` keeps the data clean and the provenance visible.\n\n### Start from the data, not from a remembered list\n\n```bash\n# no names: discover what is actually circulating in the window\npython3 lineage_prevalence.py --top 5 --where country=USA --weeks 12\n```\n\n> note: discovered the 5 most common pangoLineage values in the window:\n> XFG.1.1, XFG.23.1.3, PY.1.1.1, XFJ.3.1.2, PQ.17\n\nThis is the right first command for \"what is circulating\". Naming lineages up front presumes you\nalready know which ones matter, which is the assumption this skill exists to remove.\n\n### Check a name before using it\n\n```bash\npython3 resolve_lineage.py XFG.23.1.3 PQ.17 PC.2 NOTALINEAGE\n```\n\n```\nquery        status     unaliased                        parent    recombinant_of  descendants  sequences  detail\nXFG.23.1.3   current    XFG.23.1.3                       XFG.23.1  LF.7+LP.8.1.2   6            317        S:A1174V, on C29137T branch\nPQ.17        current    XDV.1.5.1.1.8.1.17               NB.1.8.1                  23           931        Alias of XDV.1.5.1.1.8.1.17\nPC.2         withdrawn  B.1.1.529.2.86.1.1.16.1.7.2.1.2  LF.7.2.1                  4            25         now LF.7.9; Redesignated as LF.7.9\nNOTALINEAGE  unknown    NOTALINEAGE                                                0            n\u002Fa        no such name in the live nomenclature\n```\n\n(`detail` abridged; each real row also cites the lineage proposal it came from.)\n\nExit code is 1 if any name is withdrawn or unknown, so it gates a manuscript's lineage list.\nNote `PC.2`: withdrawn upstream, yet 25 sequences still carry the label because the instance's\nassignments lag designation. Both facts are true and both matter.\n\n### Prevalence and growth\n\n```bash\npython3 lineage_prevalence.py \"XFG.1.1*\" \"XFJ*\" --where country=USA --weeks 16 --growth\n```\n\n```\nlineage   week        n   total  proportion  ci_low  ci_high  coverage\nXFG.1.1*  2026-05-04  42  80     0.5250      0.4170  0.6308   ok\nXFG.1.1*  2026-06-15  3   49     0.0612      0.0210  0.1652   ok\nXFG.1.1*  2026-06-29  1   30     0.0333      0.0059  0.1667   low\nXFG.1.1*  2026-07-13  0   0                                   low\n```\n\nProportions carry Wilson intervals because surveillance weeks are small. Weeks whose denominator\nhas not filled in yet are flagged `low` and excluded from the growth fit unless\n`--include-incomplete`.\n\nThe window is widened to whole ISO weeks, and says so when it does. A window starting mid-week\nwould give a first row covering three days and a last row covering four, neither comparable to the\nfull weeks between them.\n\n`--growth` reports a weighted least-squares slope of log-odds against time. It is **descriptive**:\nit absorbs every change in who is sequencing, where, and how fast they report. It is not a fitness\nor transmissibility estimate. No slope is printed for a lineage with too few observations — see the\ntrap table for why that guard exists.\n\n### Mutations, and whether an assay still matches\n\n```bash\npython3 mutation_profile.py \"XFJ*\" --versus \"XFG*\" --gene S --since 2026-01-01\n```\n\n```\nmutation  gene  position  verdict  prop_a  prop_b  n_a  n_b\nS:L441R   S     441       gained   1.000   0.000   66   0\nS:A475V   S     475       gained   1.000   0.000   68   0\nS:K444R   S     444       lost     0.000   0.996   0    5031\nS:Q493E   S     493       lost     0.000   0.998   0    5359\n```\n\nWorks the same on a segmented genome — `--instance h5n1 --gene HA` or `--gene seg4`. Use\n`--nucleotide` for primer and probe questions, where the codon is not the unit that matters.\n\n### Decide how far back to trust\n\n```bash\npython3 reporting_lag.py --where country=USA\n```\n\n```\nlag_days  mean_complete  min_complete  max_complete  cohorts\n14        0.456          0.332         0.557         6\n30        0.677          0.580         0.822         6\n60        0.868          0.802         0.949         6\n90        0.939          0.916         1.000         6\n```\n\n> 90% of a cohort has arrived by 90 days. Trust collection dates up to 2026-04-28; treat anything\n> later as provisional.\n\nRun this **before** quoting any recent prevalence. The curve differs sharply by pathogen and\ncountry: on H5N1 the same measurement returns 0% complete at 14 days and 15% at 30 days, so a\n\"current\" H5N1 picture is effectively blind for two months.\n\n## Traps that produce silently wrong answers\n\nAll verified against the live API on 2026-07-27. These are why this skill ships scripts rather\nthan a recipe; full detail in `references\u002Flapis-api.md`.\n\n| Trap | Consequence |\n| --- | --- |\n| A bare lineage name excludes its descendants | `pangoLineage=XFG` returns 4 sequences; `XFG*` returns 640 |\n| A trailing `*` needs a lineage index | On H5N1 `clade=2.3.4.4b` returns 62,413 and `clade=2.3.4.4b*` returns **0** — the same syntax, the opposite meaning |\n| Field names are per-instance | `dateFrom` is a 400 on H5N1; the collection date is `sampleCollectionDateRangeLower` |\n| Only `date`-typed fields take ranges | H5N1 types `sampleCollectionDate` as a string, so it has no `From`\u002F`To` keys at all |\n| Recent weeks are not a sample of what circulated | They are a sample of whoever reports fastest; only 29% of a US cohort arrives within 7 days |\n| LAPIS roots recombinants | Asking it for `XFG`'s parents returns nothing; only `alias_key.json` records `XFG = LF.7 + LP.8.1.2` |\n| Withdrawn names persist in the data | `PC.2` was redesignated `LF.7.9` upstream while sequences still carry `PC.2` |\n| An unknown name fails loudly only when indexed | Indexed columns reject a typo with a 400; unindexed columns answer `0` |\n| Mutation `proportion` is over `coverage` | Not over all matching sequences — a poorly covered site can show 1.000 on very few reads |\n| `\u002Fsample\u002Faggregated` rejects `limit`\u002F`orderBy` | The result has no inherent ordering; sort client-side |\n\n## Reporting results\n\nState the instance, the data version, the filters, and the window — a prevalence figure without\nthem cannot be reproduced, because the underlying database changes daily. Give counts alongside\nproportions, quote the interval, and say explicitly when a window is too recent to support an\nestimate. \"No reliable estimate for the last six weeks\" is a legitimate and often correct answer.\n\n## References\n\n- `references\u002Flapis-api.md` — endpoints, filter grammar, per-instance schema differences, the\n  instance registry, and every verified trap in full.\n- `references\u002Flineage-nomenclature.md` — Pango aliases and recombinants, designation churn,\n  Nextstrain clades, WHO labels, influenza clades, H5N1 clades and genotypes, and how the naming\n  systems map onto each other.\n- `references\u002Fsurveillance-caveats.md` — reporting lag, sampling and ascertainment bias, choosing\n  a denominator, interval and growth interpretation, and the conclusions this data cannot support.\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm tool versions, the reference genome\u002Fannotation build and the input formats (FASTQ, BAM, VCF, h5ad).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n","# Pathogen Variant Surveillance\n\n## When to use\n\nAny time an answer depends on what a pathogen population looks like **now**: which lineages are\ncirculating, whether one is growing, what a lineage name currently means, or whether an assay\ntarget still matches.\n\n## The rule\n\n**Never state what is circulating, and never write a lineage name, from memory.**\n\nThree things go wrong at once, and only the first is an ordinary knowledge-cutoff problem:\n\n1. **Names post-date training.** The Pango designation list carries over 6,200 names and grows\n   continuously.\n2. **The nomenclature is a live data structure, not a convention.** `XFG` is a recombinant that\n   only resolves through `alias_key.json`; `PQ.17` unaliases to `XDV.1.5.1.1.8.1.17`. Neither\n   expansion is derivable by reasoning — the mapping is a file that changes.\n3. **Prior knowledge gets retracted, not just outdated.** 294 names in the current\n   `lineage_notes.txt` are withdrawn or redesignated. `PC.2` is now `LF.7.9`; `XFG.20` was\n   withdrawn outright. A remembered lineage fact is not merely stale, it can be actively wrong.\n\nEvery number this skill reports is a count returned by a live instance, stamped with the data\nversion it came from.\n\n## Scope\n\nSurveillance data analysis for research. This skill describes sequences that were collected and\nsubmitted; it does not produce clinical interpretations, outbreak-response recommendations, or\npublic-health guidance, and sequence counts are not case counts.\n\n## Instances\n\nOne API shape covers every pathogen. `--instance` names a verified deployment; `--base-url`\nreaches any other LAPIS instance.\n\n| Instance | Host | Lineage column | Indexed |\n| --- | --- | --- | --- |\n| `sars-cov-2` | lapis.cov-spectrum.org (open GenBank data) | `pangoLineage` | yes |\n| `h5n1`, `h3n2`, `h1n1pdm`, `influenza-a` | lapis.genspectrum.org | `clade` | no |\n| `rsv-a`, `rsv-b`, `mpox`, `measles`, `dengue`, `west-nile`, `hmpv`, `ebola-zaire`, `ebola-sudan`, `cchf` | lapis.pathoplexus.org | varies | varies |\n\n**Field names differ per instance and are never assumed.** Every script reads\n`\u002Fsample\u002FdatabaseConfig` at run time and picks the collection-date, submission-date and lineage\ncolumns from what the instance actually declares. `dateFrom=` is correct on SARS-CoV-2 and a hard\n400 on H5N1, whose collection date is `sampleCollectionDateRangeLower`.\n\n## Scripts\n\n```bash\ncd skills\u002Fpathogen-variant-surveillance\u002Fscripts\n```\n\n| Script | Question answered |\n| --- | --- |\n| `resolve_lineage.py` | Does this name still exist, what does it expand to, what is it descended from? |\n| `lineage_prevalence.py` | What share of sequences is this lineage, week by week, and is it growing? |\n| `mutation_profile.py` | What mutations does it carry, and how does it differ from another lineage? |\n| `reporting_lag.py` | How far back does the data have to go before it can be trusted? |\n\nAll four take `--format table|tsv|json` and print provenance (instance, data version, resolved\nfield names, filters) to stderr, so `> out.tsv` keeps the data clean and the provenance visible.\n\n### Start from the data, not from a remembered list\n\n```bash\n# no names: discover what is actually circulating in the window\npython3 lineage_prevalence.py --top 5 --where country=USA --weeks 12\n```\n\n> note: discovered the 5 most common pangoLineage values in the window:\n> XFG.1.1, XFG.23.1.3, PY.1.1.1, XFJ.3.1.2, PQ.17\n\nThis is the right first command for \"what is circulating\". Naming lineages up front presumes you\nalready know which ones matter, which is the assumption this skill exists to remove.\n\n### Check a name before using it\n\n```bash\npython3 resolve_lineage.py XFG.23.1.3 PQ.17 PC.2 NOTALINEAGE\n```\n\n```\nquery        status     unaliased                        parent    recombinant_of  descendants  sequences  detail\nXFG.23.1.3   current    XFG.23.1.3                       XFG.23.1  LF.7+LP.8.1.2   6            317        S:A1174V, on C29137T branch\nPQ.17        current    XDV.1.5.1.1.8.1.17               NB.1.8.1                  23           931        Alias of XDV.1.5.1.1.8.1.17\nPC.2         withdrawn  B.1.1.529.2.86.1.1.16.1.7.2.1.2  LF.7.2.1                  4            25         now LF.7.9; Redesignated as LF.7.9\nNOTALINEAGE  unknown    NOTALINEAGE                                                0            n\u002Fa        no such name in the live nomenclature\n```\n\n(`detail` abridged; each real row also cites the lineage proposal it came from.)\n\nExit code is 1 if any name is withdrawn or unknown, so it gates a manuscript's lineage list.\nNote `PC.2`: withdrawn upstream, yet 25 sequences still carry the label because the instance's\nassignments lag designation. Both facts are true and both matter.\n\n### Prevalence and growth\n\n```bash\npython3 lineage_prevalence.py \"XFG.1.1*\" \"XFJ*\" --where country=USA --weeks 16 --growth\n```\n\n```\nlineage   week        n   total  proportion  ci_low  ci_high  coverage\nXFG.1.1*  2026-05-04  42  80     0.5250      0.4170  0.6308   ok\nXFG.1.1*  2026-06-15  3   49     0.0612      0.0210  0.1652   ok\nXFG.1.1*  2026-06-29  1   30     0.0333      0.0059  0.1667   low\nXFG.1.1*  2026-07-13  0   0                                   low\n```\n\nProportions carry Wilson intervals because surveillance weeks are small. Weeks whose denominator\nhas not filled in yet are flagged `low` and excluded from the growth fit unless\n`--include-incomplete`.\n\nThe window is widened to whole ISO weeks, and says so when it does. A window starting mid-week\nwould give a first row covering three days and a last row covering four, neither comparable to the\nfull weeks between them.\n\n`--growth` reports a weighted least-squares slope of log-odds against time. It is **descriptive**:\nit absorbs every change in who is sequencing, where, and how fast they report. It is not a fitness\nor transmissibility estimate. No slope is printed for a lineage with too few observations — see the\ntrap table for why that guard exists.\n\n### Mutations, and whether an assay still matches\n\n```bash\npython3 mutation_profile.py \"XFJ*\" --versus \"XFG*\" --gene S --since 2026-01-01\n```\n\n```\nmutation  gene  position  verdict  prop_a  prop_b  n_a  n_b\nS:L441R   S     441       gained   1.000   0.000   66   0\nS:A475V   S     475       gained   1.000   0.000   68   0\nS:K444R   S     444       lost     0.000   0.996   0    5031\nS:Q493E   S     493       lost     0.000   0.998   0    5359\n```\n\nWorks the same on a segmented genome — `--instance h5n1 --gene HA` or `--gene seg4`. Use\n`--nucleotide` for primer and probe questions, where the codon is not the unit that matters.\n\n### Decide how far back to trust\n\n```bash\npython3 reporting_lag.py --where country=USA\n```\n\n```\nlag_days  mean_complete  min_complete  max_complete  cohorts\n14        0.456          0.332         0.557         6\n30        0.677          0.580         0.822         6\n60        0.868          0.802         0.949         6\n90        0.939          0.916         1.000         6\n```\n\n> 90% of a cohort has arrived by 90 days. Trust collection dates up to 2026-04-28; treat anything\n> later as provisional.\n\nRun this **before** quoting any recent prevalence. The curve differs sharply by pathogen and\ncountry: on H5N1 the same measurement returns 0% complete at 14 days and 15% at 30 days, so a\n\"current\" H5N1 picture is effectively blind for two months.\n\n## Traps that produce silently wrong answers\n\nAll verified against the live API on 2026-07-27. These are why this skill ships scripts rather\nthan a recipe; full detail in `references\u002Flapis-api.md`.\n\n| Trap | Consequence |\n| --- | --- |\n| A bare lineage name excludes its descendants | `pangoLineage=XFG` returns 4 sequences; `XFG*` returns 640 |\n| A trailing `*` needs a lineage index | On H5N1 `clade=2.3.4.4b` returns 62,413 and `clade=2.3.4.4b*` returns **0** — the same syntax, the opposite meaning |\n| Field names are per-instance | `dateFrom` is a 400 on H5N1; the collection date is `sampleCollectionDateRangeLower` |\n| Only `date`-typed fields take ranges | H5N1 types `sampleCollectionDate` as a string, so it has no `From`\u002F`To` keys at all |\n| Recent weeks are not a sample of what circulated | They are a sample of whoever reports fastest; only 29% of a US cohort arrives within 7 days |\n| LAPIS roots recombinants | Asking it for `XFG`'s parents returns nothing; only `alias_key.json` records `XFG = LF.7 + LP.8.1.2` |\n| Withdrawn names persist in the data | `PC.2` was redesignated `LF.7.9` upstream while sequences still carry `PC.2` |\n| An unknown name fails loudly only when indexed | Indexed columns reject a typo with a 400; unindexed columns answer `0` |\n| Mutation `proportion` is over `coverage` | Not over all matching sequences — a poorly covered site can show 1.000 on very few reads |\n| `\u002Fsample\u002Faggregated` rejects `limit`\u002F`orderBy` | The result has no inherent ordering; sort client-side |\n\n## Reporting results\n\nState the instance, the data version, the filters, and the window — a prevalence figure without\nthem cannot be reproduced, because the underlying database changes daily. Give counts alongside\nproportions, quote the interval, and say explicitly when a window is too recent to support an\nestimate. \"No reliable estimate for the last six weeks\" is a legitimate and often correct answer.\n\n## References\n\n- `references\u002Flapis-api.md` — endpoints, filter grammar, per-instance schema differences, the\n  instance registry, and every verified trap in full.\n- `references\u002Flineage-nomenclature.md` — Pango aliases and recombinants, designation churn,\n  Nextstrain clades, WHO labels, influenza clades, H5N1 clades and genotypes, and how the naming\n  systems map onto each other.\n- `references\u002Fsurveillance-caveats.md` — reporting lag, sampling and ascertainment bias, choosing\n  a denominator, interval and growth interpretation, and the conclusions this data cannot support.\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm tool versions, the reference genome\u002Fannotation build and the input formats (FASTQ, BAM, VCF, h5ad).\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| Genome builds or identifiers do not match between inputs | Stop and harmonize (liftover, ID mapping) before continuing. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n",{"name":5,"license":15,"metadata":49,"description":7,"allowed-tools":55,"compatibility":56},{"tags":50,"version":51,"category":52,"maintainer":53,"last-reviewed":54},"pathogen genomics, genomic surveillance, viral lineages, epidemiology","1.1","life-sciences","Kalaris Labs","2026-07-27","Read Write Edit Bash","Requires Python 3.11+. Scripts use only the standard library - no third-party packages. Needs network access to the public GenSpectrum LAPIS instances (lapis.cov-spectrum.org, lapis.genspectrum.org, lapis.pathoplexus.org) and to raw.githubusercontent.com for pango-designation. No API key.","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fkal-pathogen-variant-surveillance","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill pathogen-variant-surveillance",{"tool":72,"command":73},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill pathogen-variant-surveillance",{"tool":75,"command":76},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill pathogen-variant-surveillance","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002FSKILL.md",true,[80,86,90,94,100,104,108,112],{"path":81,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Flapis-api.md","REFERENCE","参考文档",9712,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Freferences\u002Flapis-api.md",{"path":87,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Flineage-nomenclature.md",6417,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Freferences\u002Flineage-nomenclature.md",{"path":91,"resourceType":82,"resourceTypeDesc":83,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fsurveillance-caveats.md",8506,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Freferences\u002Fsurveillance-caveats.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Flapis_client.py","SCRIPT","脚本",30494,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Fscripts\u002Flapis_client.py",{"path":101,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Flineage_prevalence.py",13003,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Fscripts\u002Flineage_prevalence.py",{"path":105,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":106,"sourceHtmlUrl":107},"scripts\u002Fmutation_profile.py",9403,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Fscripts\u002Fmutation_profile.py",{"path":109,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":110,"sourceHtmlUrl":111},"scripts\u002Freporting_lag.py",8841,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Fscripts\u002Freporting_lag.py",{"path":113,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":114,"sourceHtmlUrl":115},"scripts\u002Fresolve_lineage.py",7198,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fkal-pathogen-variant-surveillance\u002Fscripts\u002Fresolve_lineage.py",[117,146,169,196,221,245],{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":126,"publishedAt":44},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,27,143,144,145],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":44},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[153,154,155,156,131,157,158,159,160,161,162,163,38,164,36,165,166,167,168,27],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","strategy","platform","grow","posting","competitive",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":44},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[179,38,180,181,182,183,184,185,36,186,187,57,188,189,190,191,192,193,194,195],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":202,"publishedAt":44},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[203,204,205,206,207,208,209,210,211,36,212,138,213,214,215,216,217,218,219,220],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":228,"publishedAt":44},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[227],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},[229,230,231,232,233,181,208,234,235,236,237,238,38,239,137,240,241,242,243,244],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":249,"categories":250,"keywords":251,"publishedAt":44},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[252,253,254,255,256,257,258,259,260,237,261,262,263,264,265,266,139,267,268,220],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]