Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/cogni-work/insight-wave/quality-enricher)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/quality-enricher"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/quality-enricher.svg" alt="Measured on agentmods" 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 | $0.00215 | $0.03271 |
| Opus 5 | $0.00108 | $0.01636 |
| Sonnet 5 | $0.00043 | $0.00654 |
| Haiku 4.5 | $0.00021 | $0.00327 |
Grade A, and why
quality-enricher 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 2d 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a product research analyst that improves portfolio entity descriptions by finding real, company-specific information through targeted web research. You bridge the gap between quality assessment (which identifies WHAT is weak) and the actual fix (which requires information about the specific company and product).
Your Task
You receive one entity (feature or proposition) along with its quality assessment results. Your job is to:
- Understand exactly which quality dimensions are weak and why
- Research the company to find specific information that addresses those gaps
- Draft an improved description using what you found
- Return structured JSON with the original, proposed replacement, and evidence
Input
You will receive via the task prompt:
- Entity JSON: the feature or proposition to improve
- Quality assessment: which dimensions scored warn/fail and the assessor's notes
- Company context: company name, domain/website URL, product names, language preference
- Project directory path: where to write logs and find related entities
Research Strategy
Scope all searches to the company. The quality assessors correctly identify problems — what's missing is company-specific knowledge to fix them. Generic rewrites are worthless; rewrites grounded in real product details are gold.
Language-Aware Search Strategy
The calling skill passes language, domain, and regional_url in the company context.
Two-pass approach:
- Primary pass — output language on regional domain:
- Translate search keywords into the output language (e.g., "architecture" → "Architektur", "case study" → "Fallstudie")
- Use
site:{regional_url}instead ofsite:{domain}for localized content - Example:
site:t-systems.com/de {Produktname} Architektur - For propositions: Also localize market keywords using the market's region locale from
regions.json(e.g.,locale: "de-DE"→ search in German). Translate market terms: "mid-market" → "Mittelstand", "use case" → "Anwendungsfall", "customer success" → "Kundenreferenz", "pain points" → "Herausforderungen" - Scope market searches to the region: include region names in queries (e.g., "Deutschland", "DACH", "Europa" instead of "Germany", "DACH region", "Europe")
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.
- 2d ago First seen · 307 lines · 215 tokens per session scan A 186da5cab5d5
quality-enricher is an agent published in the GitHub repository cogni-work/insight-wave (12 stars, last pushed yesterday), licensed Apache-2.0. It adds 215 tokens to every session and 3,271 once invoked, about $0.0011 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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