Borrowing it
Nothing to install: this file belongs to mstr-sharma/strategy-automate. 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/mstr-sharma/strategy-automate/main/AGENTS.mdgit clone --depth 1 https://github.com/mstr-sharma/strategy-automateWrote 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/instructions/mstr-sharma/strategy-automate/agents-md)<a href="https://agentmods.dev/instructions/mstr-sharma/strategy-automate/agents-md"><img src="https://agentmods.dev/badge/instructions/mstr-sharma/strategy-automate/agents-md/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/instructions/mstr-sharma/strategy-automate/agents-md"><img src="https://agentmods.dev/badge/instructions/mstr-sharma/strategy-automate/agents-md.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 | $0.04393 | $0.04393 |
| Opus 5.5 | $0.01757 | $0.01757 |
| Sonnet 5.5 | $0.00879 | $0.00879 |
| Haiku 4.5 | $0.00439 | $0.00439 |
Grade A, and why
strategy-automate AGENTS.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 18d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — canonical, LLM-agnostic entry point
You are operating inside the strategy-automate repo: skills, field-verified notes and Python helpers for automating Strategy (formerly MicroStrategy) through its REST API and Mosaic MCP server. It aims to cover every surface Strategy exposes through an API, SDK, MCP, CLI or reproducible hook: Mosaic semantic models, the classic / legacy semantic layer, runtime analytics, cubes / datasets, platform admin, AI agents, and data validation. Known gaps are tracked in memory/reference_strategy_automation_coverage.md.
This file is the canonical cross-tool entry point. Every LLM-specific shim at the repo root (CLAUDE.md, GEMINI.md, CODEX.md, GROK.md, OLLAMA.md, CURSOR.md, etc.) points here. If you are a model or tool not listed there, read this file + memory/MEMORY.md and proceed — nothing else is tool-specific.
Harness assumptions (apply across LLMs):
- The repo is plain Markdown + Python 3.9+ (standard library +
requests). No Anthropic-specific, OpenAI-specific, or Google-specific SDK calls — every helper isrequestsagainst Strategy REST; one optional variant (strategy_library_publications_mstrio.py) importsmstrio-py. $REPOin skills and notes means the root of this checkout.SKILL.mdfrontmatter (name,description) follows Anthropic's skill convention, but any harness that reads Markdown with YAML frontmatter can use it. A skill-unaware LLM can read eachSKILL.mdas a normal instruction file.memory/MEMORY.mdis a flat index with one-line hooks; any LLM cangrepor keyword-match to find the relevant memory file on demand.- Shell helpers live in
skills/strategy-platform/scripts/(sign-in, any REST operation, MCP) andskills/build-mosaic-model/scripts/(Mosaic build, inventory, mining, validation). Invoke them via whatever tool-call mechanism your harness exposes (Bash, shell, execute_command, tool-use-bash, etc.). - Configuration comes from env vars (
MSTR_BASE,MSTR_PROJECT_ID/MSTR_PROJECT_NAME,MSTR_DEST_FOLDER_ID) — seememory/reference_strategy_env.md. Never hardcode. - Sign-in: every script takes
--auth-method/MSTR_AUTH_METHOD(defaultauto) throughskills/strategy-platform/scripts/strategy_auth.py:MSTR_API_TOKEN,MSTR_USER+MSTR_PASSWORD(Standard or LDAP), or — with no credentials at all — a cached browser single-sign-on session (sso). Never type a user's password, never print tokens, never pass secrets as command-line flags. Anssosign-in needs the human to click Allow in their browser; if a command waits for it, tell them. Seememory/reference_strategy_authentication.md.
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.
- 18d ago Changed · +14 lines · +1,210 tokens per session d04796699702
- 1mo ago First seen · 122 lines · 3,183 tokens per session scan A cd61e3ca1707
strategy-automate AGENTS.md is an instructions file published in the GitHub repository mstr-sharma/strategy-automate (2 stars, last pushed yesterday), licensed MIT. It adds 4,393 tokens to every session, about $0.0176 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).