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 xSAVIKx/okf-skills --skill okf-csvgit clone --depth 1 https://github.com/xSAVIKx/okf-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/xsavikx/okf-skills/okf-csv)<a href="https://agentmods.dev/skills/xsavikx/okf-skills/okf-csv"><img src="https://agentmods.dev/badge/skills/xsavikx/okf-skills/okf-csv/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/xsavikx/okf-skills/okf-csv"><img src="https://agentmods.dev/badge/skills/xsavikx/okf-skills/okf-csv.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 | $0.00108 | $0.00760 |
| Opus 5 | $0.00054 | $0.00380 |
| Sonnet 5 | $0.00022 | $0.00152 |
| Haiku 4.5 | $0.00011 | $0.00076 |
Grade A, and why
okf-csv 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 10d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CSV OKF Connector
This skill provides a Go-based CLI tool to document a directory of CSV files as an
Open Knowledge Format (OKF) bundle: one concept per CSV, with an inferred column
schema and an optional data profile, and to sync enriched descriptions back to a
.okf-metadata.yaml sidecar.
When to Use
Use this skill when you need to:
- Catalog a folder of CSV files — each becomes a
CSV Fileconcept undertables/, with a# Columnstable whose types are inferred by sampling values. - Capture per-column statistics (
--profile) and sample rows (--sample) as grounding for enrichment. - Round-trip enriched descriptions back to the source via
.okf-metadata.yaml.
CSV has no declared types or comment store, so column types are inferred and
descriptions live in the sidecar (the same pattern okf-fs uses).
Setup
# Install the published binary (Go 1.24+):
go install github.com/xSAVIKx/okf-skills/skills/okf-csv@latest
# …or build from a clone:
cd skills/okf-csv && go build -o okf-csv .
How to Use
1. Produce an OKF bundle
./okf-csv produce --dir <csv-directory> --out <bundle-dir> [--sample <N>] [--profile]
--dir(required): directory of CSV files (traversed recursively, honoring.okfignore).--out(required): output OKF bundle directory.--sample <N>: embed up to N sample rows per file as a## Samplesection.--profile: compute per-column statistics (non-null, null, distinct, min, max, detected semantic type, low-cardinality value sets) into a## Data Profilesection.
Each data/orders.csv becomes tables/orders.md. Re-running preserves unchanged
concepts byte-for-byte (incremental produce), keeping any enriched descriptions.
2. Ingest / sync descriptions
./okf-csv ingest --dir <csv-directory> --bundle <bundle-dir> [--sync]
- Verifies each concept's columns still match its CSV header (reports drift).
--sync: writes the bundle's descriptions back to.okf-metadata.yamlin--dir.
What ships with it
9 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.
- 10d ago First seen · 74 lines · 108 tokens per session scan A 0e8d9c245544
okf-csv is a skill published in the GitHub repository xSAVIKx/okf-skills (33 stars, last pushed 10d ago), licensed Apache-2.0. It adds 108 tokens to every session and 760 once invoked, about $0.0005 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.
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