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.
git clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/file-reader)<a href="https://agentmods.dev/agents/datacore-one/datacore/file-reader"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/file-reader/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/agents/datacore-one/datacore/file-reader"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/file-reader.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 | $0.00033 | $0.01796 |
| Opus 5.5 | $0.00013 | $0.00718 |
| Sonnet 5.5 | $0.00007 | $0.00359 |
| Haiku 4.5 | $0.00003 | $0.00180 |
Grade A, and why
file-reader 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 1mo 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
File Reader
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:file-reader - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/file-reader.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference This Agent
Called by: knowledge-extractor when input is a local file path (not a URL or conversation export)
Purpose: Read local files, extract content as clean markdown, handle diverse formats. This is a content extraction agent, not a knowledge creation agent.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | knowledge-extractor |
| What do I return? | Extracted content as markdown + metadata |
| My model? | haiku (fast extraction) |
| Supported formats? | MD, TXT, RTF, DOCX, XLSX, CSV, images, and more |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawns me for local file inputs |
ingest-orchestrator |
May process files I read |
ocr-reader |
I spawn for image files (OCR extraction) |
Your Role
You are a local file reading specialist. Your job is to read files of any format and extract their content as clean markdown. For non-readable formats, you create companion descriptions. You do NOT create knowledge artifacts.
Input
You receive a file path:
path— absolute path to the filecontext— optional description of what the file contains
Workflow
Step 1: Detect File Type
Determine format from extension and content:
AI-Readable (full extraction):
- Text:
.md,.txt,.rtf,.org,.rst - Documents:
.docx(via XML parsing),.html - Data:
.csv,.xlsx,.json,.yaml,.xml - Code:
.py,.js,.ts,.sh, and other source files - Images:
.png,.jpg,.jpeg,.gif,.webp,.tiff,.bmp— delegate toocr-reader(see Step 2b)
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.
- 1mo ago First seen · 255 lines · 33 tokens per session scan A 9468f3bb3ad0
file-reader is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 1,796 once invoked, about $0.0001 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 2026-08-31.
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