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 agentmods add skills/dralkh/iktinah/bidsnpx skills add dralkh/iktinah --skill bidsgit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/bids)<a href="https://agentmods.dev/skills/dralkh/iktinah/bids"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/bids.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.08315 |
| Opus 5 | $0.00040 | $0.04158 |
| Sonnet 5 | $0.00016 | $0.01663 |
| Haiku 4.5 | $0.00008 | $0.00831 |
Grade A, and why
bids 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 2d 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 — 756 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brain Imaging Data Structure (BIDS)
Overview
The Brain Imaging Data Structure (BIDS) is a community standard for organizing and describing neuroscience and biomedical research datasets. It defines a consistent file naming convention, directory hierarchy, and metadata schema so that datasets are immediately understandable by humans and software tools alike. BIDS is governed by the BIDS Specification (currently v1.11.x) and is maintained by the community via the BIDS-Standard GitHub organization.
While BIDS originated for MRI, it has grown well beyond neuroimaging. The specification now covers 11 modalities spanning imaging, electrophysiology, and behavioral data:
- Imaging: MRI (structural, functional, diffusion, fieldmaps, perfusion/ASL), PET, microscopy
- Electrophysiology: EEG, MEG, iEEG (intracranial EEG), EMG
- Other: NIRS (near-infrared spectroscopy), motion capture, behavioral data (without imaging), MR spectroscopy
Active BEPs are extending BIDS further — notably BEP032 (microelectrode electrophysiology) will add support for extracellular recordings including Neuropixels probes, bringing BIDS to a prevalent methodology in animal neuroscience research (see also the neuropixels-analysis skill).
Adoption is required or strongly encouraged by major data repositories (OpenNeuro, DANDI), leading journals (NeuroImage, Human Brain Mapping, Scientific Data), and funding agencies (NIH, ERC).
The Python ecosystem for BIDS centers on PyBIDS (pybids) for querying and indexing BIDS datasets, and the bids-validator (Deno-based, available as PyPI package bids-validator-deno or via Deno directly) for compliance checking. Conversion from DICOM is typically done with HeuDiConv, dcm2bids, or BIDScoin.
When to Use This Skill
Apply this skill when:
- Organizing raw neuroscience data (imaging, electrophysiology, behavioral) into BIDS-compliant directory structures
- Querying an existing BIDS dataset to find specific files by subject, session, task, run, or modality
- Validating a dataset against the BIDS specification before sharing or submission
- Converting DICOM data from scanners into BIDS format
- Writing or editing JSON sidecar metadata files
- Creating BIDS-compliant derivatives (preprocessed data, analysis outputs)
- Setting up a
dataset_description.jsonfor a new dataset - Working with BIDS entities (subject, session, task, acquisition, run, etc.)
- Configuring
.bidsignoreto exclude files from validation - Preparing data for upload to OpenNeuro, DANDI, or other BIDS-aware repositories
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 756 lines · 80 tokens per session scan A 7c83b0ce6356
bids is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 8,315 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
sci-extract
Read an academic paper end to end and extract professional research insights, figures, metadata, and critique. Use this skill whenever the user shares a scientific paper, review paper, survey paper, systematic review, meta-analysis, scoping review, arXiv link, DOI, PDF, or pasted paper text and asks to read…
sci-polish
Two-stage academic paper polishing skill. Stage A reduces AI detection traces (targeting GPTZero, Turnitin, Originality.ai). Stage B performs 8-dimension quality improvement (grammar, tone, coherence, conciseness, terminology, structure, argument clarity, journal compliance). Use whenever the user asks to polish…
sci-ppt
Generate professional academic PowerPoint (PPTX) presentations from paper PDFs, structured outlines, or plain text. Use for thesis defense, seminar reports, literature presentations, and graduate school applications. Supports automatic figure extraction, LaTeX formula rendering, and bilingual (Chinese/English) layouts.
econ-compass
Your compass for navigating economics literature. Find the most important, must-read papers in any economics field or research area — from broad subfields like labor economics or macroeconomics to narrow topics like 'carbon pricing' or 'digitalization and firm innovation'. This skill curates essential reading lists by…
kami-deck
A lab-meeting deck on gut-microbiome links to sleep quality — the design, the results, the caveats, and the next experiment. Built as a decision-grade academic research deck for lab group, PI.
html-ppt-zhangzara-monochrome
A grant proposal on CRISPR base-editing for sickle-cell disease — the hypothesis, the approach, the milestones, and the risk. Built as a decision-grade academic research deck for grant review committee.