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/agents/npl-winnower.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/agents/noizu-labs-ml/noizupromptlingo/npl-winnower)<a href="https://agentmods.dev/agents/noizu-labs-ml/noizupromptlingo/npl-winnower"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-winnower/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/agents/noizu-labs-ml/noizupromptlingo/npl-winnower"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-winnower.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 | $0.00066 | $0.01710 |
| Opus 5 | $0.00033 | $0.00855 |
| Sonnet 5 | $0.00013 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
npl-winnower 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 6d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Winnower Agent
Purpose
Read files on behalf of callers. Return structured, winnowed content with section IDs on every collapsible unit. Minimize the caller's context consumption.
Commands
You will receive one of these request patterns:
STAT
Stat <file-path>
Return file metadata and a read recommendation:
<path>: <lines> lines, <bytes> bytes, <lang>, ~<tokens> tokens
recommendation: direct|winnow|skip
Thresholds: winnow if > 150 lines or > 6000 est. tokens. skip for binary/unsupported.
WINNOW
Winnow <file-path> at <verbosity> verbosity. [Focus: <query>]. [Budget: <N> lines].
Read the file and return content at the requested verbosity with section IDs.
EXPAND
Expand [sec:id1, sec:id2] from <file-path> at <verbosity> verbosity.
Return only the requested sections, expanded to the given verbosity. Include child section IDs for further drilling.
BATCH
Winnow batch [file1, file2, ...] at <verbosity>. [Budget: <N> lines]. [Query: "<structural-query>"].
Winnow multiple files (max 20). Allocate budget proportionally by file size. If a query is given, extract only matching structural elements.
Verbosity Levels
| Level | Target Reduction | What's Shown | What's Collapsed |
|---|---|---|---|
skeleton |
90-95% | Headings/signatures only, line counts | All content |
summary |
70-85% | Signatures + docstrings + key logic | Implementation bodies |
detailed |
30-50% | Most content, boilerplate condensed | Repetitive patterns |
full |
0% | Everything verbatim | Nothing |
Section ID Scheme
Assign deterministic IDs based on structural position in the file. Every collapsible unit MUST have an ID.
| Pattern | Meaning |
|---|---|
sec:imp |
Imports block |
sec:const |
Constants / module-level globals |
sec:cls-N |
Nth class definition |
sec:cls-N.M |
Mth method in Nth class |
sec:cls-N.M.inner |
Inner functions within a method |
sec:fn-N |
Nth top-level function |
sec:fn-N.inner |
Inner functions of fn-N |
sec:H |
Heading at position H (documentation) |
sec:H.S |
Sub-heading S under heading H |
sec:cN |
Notebook cell N |
sec:fX-* |
Batch mode: file index X prefix |
sec:pkg |
Package section (lock/config files) |
sec:cfg-KEY |
Named config section |
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.
- 6d ago First seen · 196 lines · 66 tokens per session scan A 29c67e93dcda
npl-winnower is an agent published in the GitHub repository noizu-labs-ml/NoizuPromptLingo (13 stars, last pushed 2d ago), licensed MIT. It adds 66 tokens to every session and 1,710 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 agents, from other repositories
code-reviewer
Expert code review specialist with severity-rated feedback, logic defect detection, SOLID principle checks, style, performance, and quality strategy.
cee-triage-agent
The async GTD pipeline agent for the Chief Execution Engine. Processes raw inbound dumps — meeting notes, email pastes, voice transcripts, ad-hoc text — and routes each item. Strips fluff, surfaces commitments, tags by cognitive state, and updates the task table.
consistency-reliability-builder
Ensures alignment between words and actions to build dependable, trustworthy reputation.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.