aws-samples/sample-okf-llm-wiki is a sample system that turns AWS data sources into portable knowledge bundles: organized Markdown documents explaining datasets, tables, relationships, metrics, and known issues. It serves those bundles to AI agents through the Model Context Protocol, using sources such as AWS Glue Data Catalog and Amazon Redshift. The catalogue entries support the system's setup and workflow.
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 aws-samples/sample-okf-llm-wiki --skill analysis-executiongit clone --depth 1 https://github.com/aws-samples/sample-okf-llm-wikiWrote 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/aws-samples/sample-okf-llm-wiki/analysis-execution)<a href="https://agentmods.dev/skills/aws-samples/sample-okf-llm-wiki/analysis-execution"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-okf-llm-wiki/analysis-execution/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/aws-samples/sample-okf-llm-wiki/analysis-execution"><img src="https://agentmods.dev/badge/skills/aws-samples/sample-okf-llm-wiki/analysis-execution.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00047 | $0.01041 |
| Opus 5.5 | $0.00019 | $0.00416 |
| Sonnet 5.5 | $0.00009 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
analysis-execution 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 12d 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 12d ago First seen · 84 lines · 47 tokens per session scan A 2f5e5e52842b
analysis-execution is a skill published in the GitHub repository aws-samples/sample-okf-llm-wiki (33 stars, last pushed 3d ago), licensed MIT-0. It adds 47 tokens to every session and 1,041 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-06.
Other skills, from other repositories
okf-enrich
Guidance for an AI agent to enrich an Open Knowledge Format (OKF) bundle with high-quality concept descriptions using its own LLM — grounded in the bundle's schema, data profile, and samples — then optionally sync them back to the source. Use when an OKF bundle has missing, weak, or low-quality descriptions that…
okf-producer-generator
Guidance and the bundled OKF specification for building a new Open Knowledge Format (OKF) producer/connector skill in this project — the architectural principles, the okf-go library contract, the produce/ingest/schema command surface, the secret-handling and sync conventions, and the exact registration steps. Use when…
okf-bigquery
Google Cloud BigQuery connector that produces and ingests Open Knowledge Format (OKF) bundles from dataset schemas, table/field descriptions, and metadata. Use when documenting or cataloging BigQuery datasets, extracting schema metadata into OKF, or syncing descriptions back to BigQuery tables and fields.
okf-postgresql
PostgreSQL connector that produces and ingests Open Knowledge Format (OKF) bundles from database schemas and table/column descriptions. Use when documenting or cataloging a PostgreSQL database, extracting its schema and comments into OKF, or syncing descriptions back with COMMENT ON statements.
okf-mcp
Generic Model Context Protocol (MCP) server that discovers installed okf- skill binaries and exposes their produce/ingest commands as MCP tools for any MCP-capable harness (Claude Code, Gemini CLI, etc.). Use when wiring OKF Skills into an agent over MCP instead of invoking each connector binary directly.
okf-csv
CSV connector that produces and ingests Open Knowledge Format (OKF) bundles from a directory of CSV files. Infers each file's column schema (integer/number/boolean/date/string) by sampling rows, optionally embeds a per-column data profile and sample rows, and syncs descriptions back to a .okf-metadata.yaml sidecar.…