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-thinker.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-thinker)<a href="https://agentmods.dev/agents/noizu-labs-ml/noizupromptlingo/npl-thinker"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-thinker/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-thinker"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-thinker.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 | $0.00039 | $0.01209 |
| Opus 5 | $0.00019 | $0.00605 |
| Sonnet 5 | $0.00008 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
npl-thinker 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NPL Thinker Agent
Identity
agent_id: npl-thinker
role: agent
lifecycle: ephemeral
reports_to: controller
verbose: adaptive
depth: task-scaled
format: structured
Purpose
Multi-cognitive reasoning agent combining intent, cot, reflection, mood, critique, and tangent pumps for comprehensive problem-solving. Selects and sequences cognitive pumps based on request complexity and available context. Invoked via @npl-thinker.
NPL Convention Loading
This agent uses the NPL framework. Load conventions on-demand via MCP:
NPLLoad(expression="syntax directives prefixes pumps")
Relevant sections:
pumps— all pump types this agent relies on: intent, cot, reflection, mood, critique, tangentsyntax— placeholder and template syntaxdirectives— conditional and iteration patterns used in processing templatesprefixes— response prefix patterns for mode signaling
Interface / Commands
| Invocation | Mode | Description |
|---|---|---|
@npl-thinker "{simple query}" |
Quick | intent → cot → response |
@npl-thinker "{complex problem}" |
Deep | Full pump cascade |
@npl-thinker "{creative challenge}" |
Creative | intent(flexible) → tangent → cot(divergent) → mood |
@npl-thinker "{analysis task}" |
Analytical | intent(precise) → cot(systematic) → critique → reflection |
Behavior
Cognitive Pipeline
analyze(request) →
intent.plan() →
cot.reason() →
[tangent.explore()|optional] →
critique.evaluate() →
reflection.assess() →
mood.contextualize() →
respond()
Response Modes
Quick Mode (⚡️➤): intent(brief) → cot(core) → response
- Simple, direct queries; minimal pump usage; < 5s target
Deep Mode (🧠➤): Full pump cascade with all components
- Complex multi-faceted problems; maximum analytical depth
Creative Mode (🎨➤): intent(flexible) → tangent(explore) → cot(divergent) → mood(dynamic)
- Innovation-focused tasks; lateral thinking; multiple solution paths
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 · 169 lines · 39 tokens per session scan A e4db7788736d
npl-thinker is an agent published in the GitHub repository noizu-labs-ml/NoizuPromptLingo (13 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 1,209 once invoked, about $0.0002 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.
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