Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bigbio/sdrf-skills --skill sdrf-campaigngit clone --depth 1 https://github.com/bigbio/sdrf-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-campaign)<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-campaign"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-campaign/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-campaign"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-campaign.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.05181 |
| Opus 5.5 | $0.00000 | $0.02072 |
| Sonnet 5.5 | $0.00000 | $0.01036 |
| Haiku 4.5 | $0.00000 | $0.00518 |
Grade A, and why
sdrf-campaign scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SDRF Campaign Protocol
A campaign has two halves and they are one pipeline: screen a candidate set down to the studies that belong, then annotate those studies in a retained- improvement loop. The screen's TSV is the loop's input.
Run both by default. Use stop_after="screen" when the user only wants a
shortlist, and point target at an existing manifest to skip straight to the
loop.
Do not guess. Verify every field against a source.
The criteria come from the user, not from this file. Nothing here is specific to any one research question.
Evidence precedence
This governs both halves and overrides any instinct to prefer structured data because it is easier to parse:
manuscript > deposit metadata (PRIDE/MassIVE structured fields + protocols)
A value the paper states is never overridden by a repository field. Repository
instruments, organisms, diseases and organismParts are submitter-chosen
dropdowns: frequently empty, sometimes wrong, and occasionally not even the
right kind of term. Measured failures from trusting them first:
instrumentsyieldingliquid chromatography separationandco-eluting ion— neither is a mass spectrometer, both are valid MS terms, and both passed ontology validation- a deposit registering one organism while the title and methods describe the organism actually analysed
- submitters registering an approximate cell line, or none, where the manuscript names the one used
Within the manuscript, Methods outrank Abstract and Results. Within the deposit,
sampleProcessingProtocol / dataProcessingProtocol free text outranks the
structured fields.
For technical metadata only, deposited search outputs (MaxQuant parameters.txt,
FragPipe fragger.params, PD .msf, DIA-NN logs) outrank the manuscript, since
they record what the search actually did.
The screen reads curated SDRFs from bigbio/sdrf-annotated-datasets first
because they are fast, not because they outrank anything: a curated SDRF is
someone's reading of these same sources, and where it disagrees with them, the
order above decides.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +2 lines 8a30eb918b8f
- 11d ago Changed · -122 lines 838dbcc7452e
- 1mo ago First seen · 584 lines · 0 tokens per session scan A 82c246c605ab
sdrf-campaign is a skill published in the GitHub repository bigbio/sdrf-skills (19 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 5,181 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-24.
Other skills, from other repositories
proteomics-data-import
Load when ingesting a MaxQuant proteinGroups.txt, FragPipe combinedprotein.tsv, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits tables/proteins.csv. Skip when raw spectra are the input (run the search engine first); the file is already OmicsClaw schema.
proteomics-de
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually); label-based TMT linear-mixed models.
proteomics-enrichment
Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Skip rank-based GSEA (use bulkrna-enrichment) or protein differential testing (use proteomics-de).
proteomics-identification
Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first); working with protein-quantification tables (use…
proteomics-ms-qc
Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first); peptide-level QC is needed (use proteomics-identification).
proteomics-ptm
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by localizationprobability), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level…