Turso is an in-process SQL database written in Rust that is compatible with SQLite and also accepts PostgreSQL syntax through an experimental frontend. It is for applications and organizations that need an embeddable database engine with support for multiple languages, platforms, and database features. The catalogue entries are skills and instructions for working with Turso.
Borrowing it
Nothing to install: this file belongs to tursodatabase/turso. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tursodatabase/turso/main/.claude/skills/index-knowledge/SKILL.mdgit clone --depth 1 https://github.com/tursodatabase/tursoWrote 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/tursodatabase/turso/index-knowledge)<a href="https://agentmods.dev/skills/tursodatabase/turso/index-knowledge"><img src="https://agentmods.dev/badge/skills/tursodatabase/turso/index-knowledge.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 | $0.00026 | $0.02764 |
| Opus 5 | $0.00013 | $0.01382 |
| Sonnet 5 | $0.00005 | $0.00553 |
| Haiku 4.5 | $0.00003 | $0.00276 |
Grade A, and why
index-knowledge 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- index-knowledge — 100% identical, 69 lines differ
How it starts
The opening of the file, as written. The whole thing — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
index-knowledge
Generate hierarchical AGENTS.md files. Root + complexity-scored subdirectories.
Usage
--create-new # Read existing → remove all → regenerate from scratch
--max-depth=2 # Limit directory depth (default: 5)
Default: Update mode (modify existing + create new where warranted)
Workflow (High-Level)
- Discovery + Analysis (concurrent)
- Launch parallel explore agents (multiple Task calls in one message)
- Main session: bash structure + LSP codemap + read existing AGENTS.md
- Score & Decide - Determine AGENTS.md locations from merged findings
- Generate - Root first, then subdirs in parallel
- Review - Deduplicate, trim, validate
TodoWrite([
{ id: "discovery", content: "Fire explore agents + LSP codemap + read existing", status: "pending", priority: "high" },
{ id: "scoring", content: "Score directories, determine locations", status: "pending", priority: "high" },
{ id: "generate", content: "Generate AGENTS.md files (root + subdirs)", status: "pending", priority: "high" },
{ id: "review", content: "Deduplicate, validate, trim", status: "pending", priority: "medium" }
])
Phase 1: Discovery + Analysis (Concurrent)
Mark "discovery" as in_progress.
Launch Parallel Explore Agents
Multiple Task calls in a single message execute in parallel. Results return directly.
// All Task calls in ONE message = parallel execution
Task(
description="project structure",
subagent_type="explore",
prompt="Project structure: PREDICT standard patterns for detected language → REPORT deviations only"
)
Task(
description="entry points",
subagent_type="explore",
prompt="Entry points: FIND main files → REPORT non-standard organization"
)
Task(
description="conventions",
subagent_type="explore",
prompt="Conventions: FIND config files (.eslintrc, pyproject.toml, .editorconfig) → REPORT project-specific rules"
)
Task(
description="anti-patterns",
subagent_type="explore",
prompt="Anti-patterns: FIND 'DO NOT', 'NEVER', 'ALWAYS', 'DEPRECATED' comments → LIST forbidden patterns"
)
Task(
description="build/ci",
subagent_type="explore",
prompt="Build/CI: FIND .github/workflows, Makefile → REPORT non-standard patterns"
)
Task(
description="test patterns",
subagent_type="explore",
prompt="Test patterns: FIND test configs, test structure → REPORT unique conventions"
)
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.
- 6d ago First seen · 359 lines · 26 tokens per session scan A c64ee52110b9
index-knowledge is a skill published in the GitHub repository tursodatabase/turso (24,185 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,764 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 2026-08-30.
Other skills, from other repositories
chdb-datastore
Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake…
chdb-sql
Use when the user wants to run SQL — especially analytical SQL — on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB — embedded ClickHouse SQL in Python with 1000+ functions, Session for…
duckdb
Use when analytical SQL must run in-process with no server: Parquet/CSV/JSON/Arrow queried in place, OLAP embedded in an app or notebook, a slow pandas groupby on multi-GB data, or S3/lakehouse data read without downloading. NOT a multi-user analytics server (that is clickhouse-analytics), NOT an app's transactional…
build-with-tinybase
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, configuring browser or database persistence, configuring MergeableStore…
graphjin-eval
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.
sq
Guides use of the sq CLI to query SQL databases and tabular files with SLQ (sq's jq-like query language) or native SQL, manage sources, choose output formats, and run inspect, diff, and table commands. Use when the user mentions sq, SLQ, wrangling CSV/Excel/JSON/DB data, cross-source joins, or command-line data…