What is mcp-data-analysis-agent copilot-instructions.md?

Repository instructions for an MCP data-analysis agent that keeps persistent engineering knowledge in an Open Knowledge Format bundle. They define how agents should record task journals, search existing knowledge, and update it.

Guiding knowledge searches, task journals, YAML metadata, links between related notes, knowledge logs, and commit practices for compatible coding agents.

They reduce repeated investigation and lost project context by giving substantial tasks a consistent journal and knowledge workflow.

How hassanvfx/mcp-data-analysis-agent configures GitHub Copilot

Reuse

Borrowing it

Nothing to install: this file belongs to hassanvfx/mcp-data-analysis-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/hassanvfx/mcp-data-analysis-agent/main/.github/copilot-instructions.md

Clone the repo

git clone --depth 1 https://github.com/hassanvfx/mcp-data-analysis-agent

Made for: GitHub Copilot.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions/github.svg)](https://agentmods.dev/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions)

Your own site

<a href="https://agentmods.dev/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions/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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/hassanvfx/mcp-data-analysis-agent/copilot-instructions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 505 This file is loaded in full into every session.

When invoked 505 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00505	$0.00505
Opus 5.5	$0.00202	$0.00202
Sonnet 5.5	$0.00101	$0.00101
Haiku 4.5	$0.00051	$0.00051

Measured 2mo ago against content hash 3bf0d3e0a948, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mcp-data-analysis-agent copilot-instructions.md 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 2mo 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.

.github/copilot-instructions.md · 36 lines

How it starts

The opening of the file, as written. The whole thing — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ClineFlow: OKF Knowledge Workflow

This project keeps persistent engineering context in the knowledge/ Open Knowledge Format (OKF) v0.2 bundle.

ChatGPT Codex and compatible agents

AGENTS.md is the shared instruction entry point for ChatGPT Codex, Cursor, and other compatible coding agents. Read clineflow/WORKING_WITH_CODEX.md for the Codex workflow; the same OKF knowledge contract also applies to Cline, Copilot, and Windsurf.

Task knowledge rules

For each substantial task, create or resume knowledge/journals/<task-name>.md using knowledge/journals/TASK_TEMPLATE.md.
Every task journal is an OKF concept. Keep its YAML frontmatter valid, retain type: Engineering Journal, and update generated.at after meaningful changes.
Use status: draft while work is active and status: stable when it is complete. Do not add verified or sources unless they are factual.
Before starting or resuming work, search knowledge/ first. If docs/journals/ exists, search it too as read-only legacy context. Never create or update new work there.
Link related concepts with normal Markdown links. Update knowledge/log.md for material knowledge changes.

Commit workflow

When the user asks to commit:

Update the active knowledge/journals/ concept with the implementation summary, decisions, tests, and next steps.
Update knowledge/log.md if the change added or materially changed knowledge.
Run ./validate-okf and resolve validation failures. When the project has optional PyYAML available, prefer ./validate-okf --strict before committing.
Stage the code and updated knowledge artifacts together, then create a descriptive commit.

Start with knowledge/index.md and descend through index.md files for progressive disclosure.
docs/journals/ is optional legacy history; it is not part of the OKF bundle and must not be passed to validate-okf.
The reference-repository system under clineflow/ remains optional and unchanged.

Read the full file on GitHub · 36 lines

Changes

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.

2mo ago First seen · 36 lines · 505 tokens per session scan A 3bf0d3e0a948

Subscribe to this mod's changes

mcp-data-analysis-agent copilot-instructions.md is an instructions file published in the GitHub repository hassanvfx/mcp-data-analysis-agent (0 stars, last pushed 2mo ago), licensed MIT. It adds 505 tokens to every session, about $0.0020 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.

Building agents Memory and context data-analysis mcp model-context-protocol postgresql sqlalchemy

mcp-data-analysis-agent copilot-instructions.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
mcp-data-analysis-agent copilot-instructions.mdhassanvfx/mcp-data-analysis-agent	Instructions file	0	2mo ago	MIT	A	505	GitHub Copilot
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next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
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vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

mcp-data-analysis-agent: Instructions file for GitHub Copilot