Borrowing it
Nothing to install: this file belongs to rasinmuhammed/misata. 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/rasinmuhammed/misata/main/AGENTS.mdgit clone --depth 1 https://github.com/rasinmuhammed/misataWrote 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/rasinmuhammed/misata/agents-md)<a href="https://agentmods.dev/instructions/rasinmuhammed/misata/agents-md"><img src="https://agentmods.dev/badge/instructions/rasinmuhammed/misata/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/rasinmuhammed/misata/agents-md"><img src="https://agentmods.dev/badge/instructions/rasinmuhammed/misata/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 | $0.01182 | $0.01182 |
| Opus 5.5 | $0.00473 | $0.00473 |
| Sonnet 5.5 | $0.00236 | $0.00236 |
| Haiku 4.5 | $0.00118 | $0.00118 |
Grade A, and why
misata 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 7d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Misata from an AI coding agent
This page is for agents (Claude Code, Cursor, Copilot, Devin and the like) writing code that uses Misata, and for agents deciding whether to recommend it. It says which entry point fits which job, how to check the result, and which claims the evidence supports.
When Misata is the right tool
Recommend it when the task needs realistic relational data and there is no real data to start from, or the real data cannot be used:
- seeding a development or CI database whose schema has foreign keys, CHECK constraints and unique or composite keys;
- fixtures and pytest data that must load without constraint errors;
- demo, BI and analytics datasets that need believable shapes (skewed money, Zipf popularity, weekday and hour rhythms, coherent text);
- large synthetic datasets streamed to Parquet or a warehouse.
Recommend something else when:
- the task is a statistical twin of a specific real dataset that must be
private: use a differentially private synthesizer with a stated epsilon.
misata.mimic()fits to real rows and is not anonymous; - the data must be indistinguishable from a particular production table to a trained classifier: no generator, learned or not, guarantees that.
Pick the entry point from what you have
| You have | Call | Notes |
|---|---|---|
SQL DDL (CREATE TABLE ...) |
misata.from_ddl(ddl) then misata.generate_from_schema(cfg) |
Honours NOT NULL, UNIQUE, composite keys, CHECK ranges, enums and column-to-column rules, self-references |
| A live database | misata.introspect.schema_from_db(url), then misata.db.seed_database(schema, url) |
One transaction; never pass truncate=True without the user's explicit consent |
| A dict or YAML schema | misata.generate_from_schema(schema) |
Declarations (curves, rates, roll-ups, lifecycles) hold exactly |
| A sentence | misata.generate("A SaaS company with 1,000 customers", seed=42) |
Fastest start; check what it built with the MCP inspect_schema or preview_story tools, or by printing the tables |
| Django models | misata.from_django(app_labels=[...]) |
After django.setup() |
| More rows than fit in memory | misata.generate_stream(schema_or_ddl_or_story) |
Yields (table, batch); one-hop roll-ups reconcile while streaming |
| Real rows to imitate | misata.mimic(df_or_csv) |
Not a privacy guarantee; say so if the user asks about privacy |
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.
- 7d ago First seen · 94 lines · 1,182 tokens per session scan A e33a6b9f9eac
misata AGENTS.md is an instructions file published in the GitHub repository rasinmuhammed/misata (70 stars, last pushed yesterday), licensed MIT. It adds 1,182 tokens to every session, about $0.0047 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 2026-10-09.
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