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-autoresearchgit 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-autoresearch)<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-autoresearch"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-autoresearch/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-autoresearch"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00048 | $0.03830 |
| Opus 5 | $0.00024 | $0.01915 |
| Sonnet 5 | $0.00010 | $0.00766 |
| Haiku 4.5 | $0.00005 | $0.00383 |
Grade A, and why
sdrf-autoresearch 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 5d ago.
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 — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SDRF Autoresearch Protocol
This workflow is a domain-specific autonomous loop for SDRF annotation. It is intended to function with minimal user supervision once the target and optimization goal are clear.
Use it when the user asks for:
- annotation of all datasets in a category
- repeated refine → validate → fix loops
- maximum metadata completion without blind guessing
- autonomous SDRF improvement until no more retained gains are possible
This is a protocol skill, not a dedicated runner script. Execute the loop by
following the steps below and by calling the existing sdrf:* skills in order.
Console Triggers
Claude-style examples:
/sdrf-skills:sdrf-autoresearch target="all PRIDE cell line datasets"
/sdrf-skills:sdrf-autoresearch target="all sandbox crosslinking datasets" profile="crosslinking"
/sdrf-skills:sdrf-autoresearch target="manifest:data/cell_line_manifest.tsv" objective="maximize_valid_field_coverage"
Codex-style examples:
$sdrf-autoresearch target="all PRIDE cell line datasets"
$sdrf-autoresearch target="accessions:PXD001234,PXD005678" profile="clinical"
$sdrf-autoresearch target="all sandbox crosslinking datasets" objective="crosslinking_assay_completion" write="sandbox"
Step 1: Parse the Request into a Loop Config
Normalize the user request into these fields:
-
target- What dataset set to operate on
- Examples:
all PRIDE cell line datasetsall sandbox crosslinking datasetsmanifest:data/cell_line_manifest.tsvaccessions:PXD001234,PXD005678
-
profile- Domain preset that biases which templates, columns, and evidence sources matter most
- Supported defaults:
general-proteomicscell-linecrosslinkingclinicalimmunopeptidomics
-
objective- The optimization target for retained improvements
- Supported defaults:
maximize_valid_field_coverageminimize_unknownscrosslinking_assay_completioncell_line_sample_completionclinical_sample_completion
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.
- 5d ago Changed 27137d853686
- 11d ago First seen · 394 lines · 48 tokens per session scan A c67e593d2773
sdrf-autoresearch is a skill published in the GitHub repository bigbio/sdrf-skills (18 stars, last pushed 5d ago), licensed MIT. It adds 48 tokens to every session and 3,830 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-30.
Other skills, from other repositories
uniprot-query
Query UniProt database for protein sequences, metadata, and search by criteria. Use this skill when: (1) Looking up protein information by UniProt accession ID, (2) Searching proteins by gene name, organism, function, or disease, (3) Retrieving comprehensive protein metadata including domains, PTMs, and annotations.
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 when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only) (use bulkrna-enrichment); rank-based GSEA.
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-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…