mcpbeat

Pathogen Variant Surveillance

k-dense-ai/pathogen-variant-surveillance

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on remembered facts - which SARS-CoV-2 variant is dominant, whether a Pango lineage is still designated or has been withdrawn, what clade or genotype of H5N1 is in a host or region, whether a PCR primer or assay target still matches circulating sequence, or how a lineage's prevalence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/mpox/measles/dengue lineage", "CoV-Spectrum", "LAPIS", "Nextclade", "pango-designation", and any request to report what a pathogen population looks like today.

26k tokens
context cost
the whole folder, loaded on every use
9
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ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
32514
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on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill pathogen-variant-surveillance

The instruction itself

14 sections, as written by the author

Pathogen Variant Surveillance

When to use

Any time an answer depends on what a pathogen population looks like now: which lineages are

circulating, whether one is growing, what a lineage name currently means, or whether an assay

target still matches.

The rule

Never state what is circulating, and never write a lineage name, from memory.

Three things go wrong at once, and only the first is an ordinary knowledge-cutoff problem:

  • Names post-date training. The Pango designation list carries over 6,200 names and grows

continuously.

  • The nomenclature is a live data structure, not a convention. XFG is a recombinant that

only resolves through alias_key.json; PQ.17 unaliases to XDV.1.5.1.1.8.1.17. Neither

expansion is derivable by reasoning — the mapping is a file that changes.

  • Prior knowledge gets retracted, not just outdated. 294 names in the current

lineage_notes.txt are withdrawn or redesignated. PC.2 is now LF.7.9; XFG.20 was

withdrawn outright. A remembered lineage fact is not merely stale, it can be actively wrong.

Every number this skill reports is a count returned by a live instance, stamped with the data

version it came from.

Scope

Surveillance data analysis for research. This skill describes sequences that were collected and

submitted; it does not produce clinical interpretations, outbreak-response recommendations, or

public-health guidance, and sequence counts are not case counts.

Instances

One API shape covers every pathogen. --instance names a verified deployment; --base-url

reaches any other LAPIS instance.

| Instance | Host | Lineage column | Indexed |

| --- | --- | --- | --- |

| sars-cov-2 | lapis.cov-spectrum.org (open GenBank data) | pangoLineage | yes |

| h5n1, h3n2, h1n1pdm, influenza-a | lapis.genspectrum.org | clade | no |

| rsv-a, rsv-b, mpox, measles, dengue, west-nile, hmpv, ebola-zaire, ebola-sudan, cchf | lapis.pathoplexus.org | varies | varies |

Field names differ per instance and are never assumed. Every script reads

/sample/databaseConfig at run time and picks the collection-date, submission-date and lineage

columns from what the instance actually declares. dateFrom= is correct on SARS-CoV-2 and a hard

400 on H5N1, whose collection date is sampleCollectionDateRangeLower.

Scripts

cd skills/pathogen-variant-surveillance/scripts

| Script | Question answered |

| --- | --- |

| resolve_lineage.py | Does this name still exist, what does it expand to, what is it descended from? |

| lineage_prevalence.py | What share of sequences is this lineage, week by week, and is it growing? |

| mutation_profile.py | What mutations does it carry, and how does it differ from another lineage? |

| reporting_lag.py | How far back does the data have to go before it can be trusted? |

All four take --format table|tsv|json and print provenance (instance, data version, resolved

field names, filters) to stderr, so > out.tsv keeps the data clean and the provenance visible.

Start from the data, not from a remembered list

# no names: discover what is actually circulating in the window
python3 lineage_prevalence.py --top 5 --where country=USA --weeks 12

> note: discovered the 5 most common pangoLineage values in the window:

> XFG.1.1, XFG.23.1.3, PY.1.1.1, XFJ.3.1.2, PQ.17

This is the right first command for "what is circulating". Naming lineages up front presumes you

already know which ones matter, which is the assumption this skill exists to remove.

Check a name before using it

python3 resolve_lineage.py XFG.23.1.3 PQ.17 PC.2 NOTALINEAGE
query        status     unaliased                        parent    recombinant_of  descendants  sequences  detail
XFG.23.1.3   current    XFG.23.1.3                       XFG.23.1  LF.7+LP.8.1.2   6            317        S:A1174V, on C29137T branch
PQ.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
PC.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
NOTALINEAGE  unknown    NOTALINEAGE                                                0            n/a        no such name in the live nomenclature

(detail abridged; each real row also cites the lineage proposal it came from.)

Exit code is 1 if any name is withdrawn or unknown, so it gates a manuscript's lineage list.

Note PC.2: withdrawn upstream, yet 25 sequences still carry the label because the instance's

assignments lag designation. Both facts are true and both matter.

Prevalence and growth

python3 lineage_prevalence.py "XFG.1.1*" "XFJ*" --where country=USA --weeks 16 --growth
lineage   week        n   total  proportion  ci_low  ci_high  coverage
XFG.1.1*  2026-05-04  42  80     0.5250      0.4170  0.6308   ok
XFG.1.1*  2026-06-15  3   49     0.0612      0.0210  0.1652   ok
XFG.1.1*  2026-06-29  1   30     0.0333      0.0059  0.1667   low
XFG.1.1*  2026-07-13  0   0                                   low

Proportions carry Wilson intervals because surveillance weeks are small. Weeks whose denominator

has not filled in yet are flagged low and excluded from the growth fit unless

--include-incomplete.

The window is widened to whole ISO weeks, and says so when it does. A window starting mid-week

would give a first row covering three days and a last row covering four, neither comparable to the

full weeks between them.

--growth reports a weighted least-squares slope of log-odds against time. It is descriptive:

it absorbs every change in who is sequencing, where, and how fast they report. It is not a fitness

or transmissibility estimate. No slope is printed for a lineage with too few observations — see the

trap table for why that guard exists.

Mutations, and whether an assay still matches

python3 mutation_profile.py "XFJ*" --versus "XFG*" --gene S --since 2026-01-01
mutation  gene  position  verdict  prop_a  prop_b  n_a  n_b
S:L441R   S     441       gained   1.000   0.000   66   0
S:A475V   S     475       gained   1.000   0.000   68   0
S:K444R   S     444       lost     0.000   0.996   0    5031
S:Q493E   S     493       lost     0.000   0.998   0    5359

Works the same on a segmented genome — --instance h5n1 --gene HA or --gene seg4. Use

--nucleotide for primer and probe questions, where the codon is not the unit that matters.

Decide how far back to trust

python3 reporting_lag.py --where country=USA
lag_days  mean_complete  min_complete  max_complete  cohorts
14        0.456          0.332         0.557         6
30        0.677          0.580         0.822         6
60        0.868          0.802         0.949         6
90        0.939          0.916         1.000         6

> 90% of a cohort has arrived by 90 days. Trust collection dates up to 2026-04-28; treat anything

> later as provisional.

Run this before quoting any recent prevalence. The curve differs sharply by pathogen and

country: on H5N1 the same measurement returns 0% complete at 14 days and 15% at 30 days, so a

"current" H5N1 picture is effectively blind for two months.

Traps that produce silently wrong answers

All verified against the live API on 2026-07-27. These are why this skill ships scripts rather

than a recipe; full detail in references/lapis-api.md.

| Trap | Consequence |

| --- | --- |

| A bare lineage name excludes its descendants | pangoLineage=XFG returns 4 sequences; XFG* returns 640 |

| 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 |

| Field names are per-instance | dateFrom is a 400 on H5N1; the collection date is sampleCollectionDateRangeLower |

| Only date-typed fields take ranges | H5N1 types sampleCollectionDate as a string, so it has no From/To keys at all |

| 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 |

| LAPIS roots recombinants | Asking it for XFG's parents returns nothing; only alias_key.json records XFG = LF.7 + LP.8.1.2 |

| Withdrawn names persist in the data | PC.2 was redesignated LF.7.9 upstream while sequences still carry PC.2 |

| An unknown name fails loudly only when indexed | Indexed columns reject a typo with a 400; unindexed columns answer 0 |

| Mutation proportion is over coverage | Not over all matching sequences — a poorly covered site can show 1.000 on very few reads |

| /sample/aggregated rejects limit/orderBy | The result has no inherent ordering; sort client-side |

Reporting results

State the instance, the data version, the filters, and the window — a prevalence figure without

them cannot be reproduced, because the underlying database changes daily. Give counts alongside

proportions, quote the interval, and say explicitly when a window is too recent to support an

estimate. "No reliable estimate for the last six weeks" is a legitimate and often correct answer.

References

  • references/lapis-api.md — endpoints, filter grammar, per-instance schema differences, the

instance registry, and every verified trap in full.

  • references/lineage-nomenclature.md — Pango aliases and recombinants, designation churn,

Nextstrain clades, WHO labels, influenza clades, H5N1 clades and genotypes, and how the naming

systems map onto each other.

  • references/surveillance-caveats.md — reporting lag, sampling and ascertainment bias, choosing

a denominator, interval and growth interpretation, and the conclusions this data cannot support.

How to use it

Copy the folder

Take k-dense-ai/pathogen-variant-surveillance from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.