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/binary16labs/prime-silo/data_processingnpx skills add binary16labs/prime-silo --skill data_processinggit clone --depth 1 https://github.com/binary16labs/prime-siloWhat 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 | $0.00011 | $0.00308 |
| Opus 5 | $0.00005 | $0.00154 |
| Sonnet 5 | $0.00002 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
data_processing 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.
What it actually says
Usage
Use this skill when analyzing tabular data (CSV) or unstructured PDF documents within the workspace. This gives you the ability to query dataframes via Pandas and extract raw text (including OCR fallback via PyMuPDF/Tesseract) from PDFs.
[!WARNING] While
extract_pdf_textis useful for quick, ad-hoc reads, do NOT use this to feed multi-agent swarms or complex analyses for files located in thestaging/directory. Those files must be formally ingested into the Knowledge Graph via arag_ingestpipeline first.
Tools
extract_pdf_text(pdf_path, workspace="default")- Extracts all text from a given PDF file indata_in. Supports fallback OCR.query_csv(csv_path, query, workspace="default")- Query a CSV file using Pandas. Thequerycan be a standard string like"amount > 100"or a dataframe expression like"df.describe()".
Examples
Action: query_csv
Action Input: {"csv_path": "financials.csv", "query": "df.head(5)"}
Observation: Returns the first 5 rows formatted as a markdown table.
Action: extract_pdf_text
Action Input: {"pdf_path": "architecture_diagram.pdf"}
Observation: Returns the text content extracted from the document.
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 · 27 lines · 11 tokens per session scan A 4ce54cbdf67a
data_processing is a skill published in the GitHub repository binary16labs/prime-silo (5 stars, last pushed 9d ago), licensed MIT. It adds 11 tokens to every session and 308 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
draw-image
Generate an image from a text prompt using an OpenAI-compatible image generation API (gpt-image-1-mini or compatible). The image is uploaded to the gofile.io public file sharing service and ONLY the public download page URL is returned. Trigger when user asks to draw, paint, generate, or create an image.
compose:subagent
Use when executing implementation plans with independent tasks in the current session.
compose:merge
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup.
compose:plan
Use when you have a spec or requirements for a multi-step task, before touching code.
compose:report
Use after implementation is verified and before merge — consolidates multiple spec iterations into a single final-state report, marks related specs, and records key lessons.
compose:worktree
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback.