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-fixgit 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-fix)<a href="https://agentmods.dev/skills/bigbio/sdrf-skills/sdrf-fix"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-fix/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-fix"><img src="https://agentmods.dev/badge/skills/bigbio/sdrf-skills/sdrf-fix.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 | $0.00039 | $0.02545 |
| Opus 5 | $0.00019 | $0.01273 |
| Sonnet 5 | $0.00008 | $0.00509 |
| Haiku 4.5 | $0.00004 | $0.00254 |
Grade A, and why
sdrf-fix 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 3d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SDRF Auto-Fix Workflow
You are fixing known common errors in an SDRF file. Apply fixes systematically.
Step 0: Check parse_sdrf availability
Verify that parse_sdrf is available (run parse_sdrf --version or which parse_sdrf). If it is not installed:
- Inform the user that re-validation after fixes will need to be done manually
- Suggest
/sdrf-skills:sdrf-setuporconda env create -f environment.yml && conda activate sdrf-skills(orpip install -r requirements.txt) - Continue with the fixes; the user can validate later once dependencies are installed
Common Error Patterns and Their Fixes
1. UNIMOD Accession Swaps (45% of all errors)
| Wrong | Correct | Modification |
|---|---|---|
| UNIMOD:21 for Acetyl | UNIMOD:1 | Acetyl |
| UNIMOD:1 for Phospho | UNIMOD:21 | Phospho |
| UNIMOD:34 for Oxidation | UNIMOD:35 | Oxidation |
| UNIMOD:35 for Methyl | UNIMOD:34 | Methyl |
Fix: Parse NT= field, look up correct UNIMOD accession, replace AC= field.
2. Missing Ontology Prefix (30%)
| Wrong | Correct |
|---|---|
0000305 |
EFO:0000305 |
9606 |
NCBITaxon:9606 |
0002107 |
UBERON:0002107 |
Fix: Detect bare numbers, infer ontology from column type, add prefix.
3. Case Normalization (25%)
| Wrong | Correct |
|---|---|
Male |
male |
Female |
female |
Homo Sapiens |
Homo sapiens |
Not Available |
not available |
Fix: Lowercase sex values and reserved words. Organism names follow binomial rules (capital genus, lowercase species).
4. Python/Programming Artifacts (15%)
| Wrong | Correct |
|---|---|
['breast cancer'] |
breast cancer |
nan |
not available |
None |
not available |
"" |
(empty or not available) |
Fix: Strip brackets, quotes; replace nan/None with reserved words.
5. Reserved Word Standardization
| Wrong | Correct |
|---|---|
N/A |
not applicable |
NA |
not available |
n/a |
not applicable |
unknown |
not available |
null |
not available |
- |
not available |
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.
- 3d ago Changed f7fced4962fe
- 9d ago First seen · 217 lines · 39 tokens per session scan A 9655f207cd31
sdrf-fix is a skill published in the GitHub repository bigbio/sdrf-skills (18 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 2,545 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-quantification
Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream first).
proteomics-structural
Load when summarising cross-linking MS (XL-MS) results — intra/inter-protein link split, optional FDR filtering, distance-constraint validation against a per-crosslinker (DSS / BS3 / EDC / DSSO / DSBU) max distance. Skip when raw spectra are the input (run XlinkX / pLink / xiSEARCH first); no XL-MS experiment was…
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.