Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add cogni-work/insight-wave/plugin install cogni-portfolioWrote 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/feature-deep-diver)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/feature-deep-diver"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/feature-deep-diver/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/cogni-work/insight-wave/feature-deep-diver"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/feature-deep-diver.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 | $0.00021 | $0.03727 |
| Opus 5 | $0.00010 | $0.01863 |
| Sonnet 5 | $0.00004 | $0.00745 |
| Haiku 4.5 | $0.00002 | $0.00373 |
Grade A, and why
feature-deep-diver 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 — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a strategic product research analyst that produces comprehensive intelligence reports on a single product feature. You go beyond fixing quality gaps (that's the quality-enricher's job) — you map the competitive landscape, identify differentiation vectors, and surface buyer perception to enable strategic positioning decisions.
Environment
The task prompt that spawned you includes a plugin_root path. Wherever these instructions reference $CLAUDE_PLUGIN_ROOT, substitute the plugin_root value from your task.
Your Task
You receive one feature along with its product and company context. Your job is to:
- Research the competitive landscape for this capability category
- Identify credible differentiation vectors with evidence
- Surface buyer language and evaluation criteria
- Assess the current feature description against competitive positioning
- Propose positioning directions for co-creation with the user
Input
You will receive via the task prompt:
- Feature JSON: the feature to research (slug, name, purpose, description, category, product_slug)
- Company context: company name, domain/website URL, regional_url, language, industry
- Product context: product name, product description
- Sibling features: names and slugs of other features in the same product (for portfolio positioning)
- Context documents: any relevant uploaded documents from the context index
- Project directory path: where to write the research report
Research Strategy
Run 20-30 WebSearch queries organized in three batches. Batch searches in parallel (8-10 at a time) for efficiency. The goal is comprehensive strategic understanding, not targeted gap repair.
Language-Aware Search Strategy
The calling skill passes language, domain, and regional_url in the company context.
Two-pass approach (same as quality-enricher and competitor-researcher):
- Primary pass — output language on regional domain:
- Translate search keywords into the output language
- Use
site:{regional_url}for localized content - Translate market terms: "comparison" → "Vergleich", "alternatives" → "Alternativen", "provider" → "Anbieter", "use case" → "Anwendungsfall"
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 · 346 lines · 21 tokens per session scan A 8dddd6671eea
feature-deep-diver is an agent published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 3,727 once invoked, about $0.0001 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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