Borrowing it
Nothing to install: this file belongs to noizu-labs-ml/NoizuPromptLingo. 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/noizu-labs-ml/NoizuPromptLingo/main/.claude/commands/generate-meta-data.mdgit clone --depth 1 https://github.com/noizu-labs-ml/NoizuPromptLingoWrote 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/commands/noizu-labs-ml/noizupromptlingo/generate-meta-data)<a href="https://agentmods.dev/commands/noizu-labs-ml/noizupromptlingo/generate-meta-data"><img src="https://agentmods.dev/badge/commands/noizu-labs-ml/noizupromptlingo/generate-meta-data/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/commands/noizu-labs-ml/noizupromptlingo/generate-meta-data"><img src="https://agentmods.dev/badge/commands/noizu-labs-ml/noizupromptlingo/generate-meta-data.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 | $0.00061 | $0.01756 |
| Opus 5.5 | $0.00024 | $0.00702 |
| Sonnet 5.5 | $0.00012 | $0.00351 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
generate-meta-data 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.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metadata Generation Agent Instructions
Generate and update structured metadata files for this project. Follow these conventions precisely. Use sub-agents @npl-technical-writer for prep files in parallel; use git-tree with file type filters to prepare specified file types.
Sub-agent prompts should reference the generate-meta-data command definition for context.
Naming Conventions
| Target Type | Metadata Filename | Location |
|---|---|---|
| File | .{filename}.meta.md |
Same directory as the source file |
| Directory | .dir.meta.md |
Inside the target directory |
Examples:
src/utils.py→src/.utils.py.meta.mdsrc/models/→src/models/.dir.meta.md
File Metadata Template
For each source file, generate a corresponding .{filename}.meta.md if not present — otherwise check the checksum with yq and if different from what is stored, refresh:
file: {filename}
md5: {md5_hash_of_file_contents}
changeset: {git_commit_sha_or_"uncommitted"}
---
# Contents
## Summary
[...| 1-3 sentences: What does this file do? What is its role in the system?]
## Layout
```mermaid
classDiagram
class ClassName {
+publicMethod(args): returnType
-privateMethod()
#protectedField: type
~packagePrivate()
}
```
<!-- For non-OOP files, use: -->
```mermaid
flowchart TD
subgraph Functions
funcA["funcA(x, y) -> int"]
funcB["funcB(data) -> None"]
end
subgraph Constants
CONST_A
CONST_B
end
```
## Review
- [ ] TODO: {pending tasks}
- ⚠️ Issue: {bugs, code smells, security concerns}
- 💡 Note: {observations, suggestions, context}
# Links
## Outgoing
```mermaid
flowchart LR
ThisFile["this_file.py"] --> Import1["module_a"]
ThisFile --> Import2["module_b.ClassName"]
ThisFile --> Import3["external_lib"]
```
## Incoming
```mermaid
flowchart RL
Consumer1["caller_a.py"] --> ThisFile["this_file.py"]
Consumer2["test_this_file.py"] --> ThisFile
```
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 · 275 lines · 61 tokens per session scan A 46e7a22f64f0
generate-meta-data is a command published in the GitHub repository noizu-labs-ml/NoizuPromptLingo (13 stars, last pushed 10d ago), licensed MIT. It adds 61 tokens to every session and 1,756 once invoked, about $0.0002 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 2026-09-04.
generate-meta-data compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| generate-meta-datanoizu-labs-ml/NoizuPromptLingo | Command | 13 | 10d ago | MIT | A | 61 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |