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 | $0.00030 | $0.00878 |
| Sonnet 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 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 — 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.
- 7d 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 2d ago), licensed MIT. It adds 61 tokens to every session and 1,756 once invoked, about $0.0003 per session on Opus 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.