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 cocoindex-io/cocoindex-claude --skill cocoindexgit clone --depth 1 https://github.com/cocoindex-io/cocoindex-claudeWrote 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/cocoindex-io/cocoindex-claude/cocoindex)<a href="https://agentmods.dev/skills/cocoindex-io/cocoindex-claude/cocoindex"><img src="https://agentmods.dev/badge/skills/cocoindex-io/cocoindex-claude/cocoindex/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/cocoindex-io/cocoindex-claude/cocoindex"><img src="https://agentmods.dev/badge/skills/cocoindex-io/cocoindex-claude/cocoindex.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 | $0.00072 | $0.05961 |
| Opus 5 | $0.00036 | $0.02981 |
| Sonnet 5 | $0.00014 | $0.01192 |
| Haiku 4.5 | $0.00007 | $0.00596 |
Grade A, and why
cocoindex 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 9d 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:
- cocoindex — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 832 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CocoIndex
Overview
CocoIndex is an ultra-performant real-time data transformation framework for AI with incremental processing. This skill enables building indexing flows that extract data from sources, apply transformations (chunking, embedding, LLM extraction), and export to targets (vector databases, graph databases, relational databases).
Core capabilities:
- Write indexing flows - Define ETL pipelines using Python
- Create custom functions - Build reusable transformation logic
- Operate flows - Run and manage flows using CLI or Python API
Key features:
- Incremental processing (only processes changed data)
- Live updates (continuously sync source changes to targets)
- Built-in functions (text chunking, embeddings, LLM extraction)
- Multiple data sources (local files, S3, Azure Blob, Google Drive, Postgres)
- Multiple targets (Postgres+pgvector, Qdrant, LanceDB, Neo4j, Kuzu)
For detailed documentation: https://cocoindex.io/docs/ Search documentation: https://cocoindex.io/docs/search?q=url%20encoded%20keyword
When to Use This Skill
Use when users request:
- "Build a vector search index for my documents"
- "Create an embedding pipeline for code/PDFs/images"
- "Extract structured information using LLMs"
- "Build a knowledge graph from documents"
- "Set up live document indexing"
- "Create custom transformation functions"
- "Run/update my CocoIndex flow"
Flow Writing Workflow
Step 1: Understand Requirements
Ask clarifying questions to understand:
Data source:
- Where is the data? (local files, S3, database, etc.)
- What file types? (text, PDF, JSON, images, code, etc.)
- How often does it change? (one-time, periodic, continuous)
Transformations:
- What processing is needed? (chunking, embedding, extraction, etc.)
- Which embedding model? (SentenceTransformer, OpenAI, custom)
- Any custom logic? (filtering, parsing, enrichment)
Target:
- Where should results go? (Postgres, Qdrant, Neo4j, etc.)
- What schema? (fields, primary keys, indexes)
- Vector search needed? (specify similarity metric)
What ships with it
4 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.
- 9d ago First seen · 832 lines · 72 tokens per session scan A d5f15a9e07fd
cocoindex is a skill published in the GitHub repository cocoindex-io/cocoindex-claude (79 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 5,961 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 2026-08-30.
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