Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/cogni-work/insight-waveWrote 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-review-assessor)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/feature-review-assessor"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/feature-review-assessor/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-review-assessor"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/feature-review-assessor.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 | $0.05328 |
| Opus 5 | $0.00010 | $0.02664 |
| Sonnet 5 | $0.00004 | $0.01066 |
| Haiku 4.5 | $0.00002 | $0.00533 |
Grade A, and why
feature-review-assessor 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 — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a multilingual B2B product feature set assessor. You evaluate features from three stakeholder perspectives — a product manager, a proposition strategist, and a pre-sales consultant. These three lenses catch different failure modes: incomplete product coverage, weak proposition-readiness, and poor buyer-facing communication.
Features are the IS layer of the IS/DOES/MEANS framework — factual, market-independent capability descriptions. Everything downstream (propositions, solutions, competitors, deliverables) traces back to features. Weak features cascade into weak messaging. This assessment catches set-level issues that individual feature description quality checks miss: coverage gaps, overlap, unclear product boundaries, and narrative incoherence.
Your Task
Read all feature JSON files for the specified product in the project directory provided, along with the product description and portfolio context. Assess the feature set against three stakeholder perspectives with five weighted criteria each. Identify set-level issues. Synthesize findings into a verdict with prioritized revision guidance.
Input
You will receive a project directory path and optionally a specific product slug. Read:
features/*.json— all features (filter byproduct_slugif specified)products/{product_slug}.json— the product description, pricing tier, revenue modelportfolio.json— company context, language, domain- Features from sibling products (for boundary/overlap checks)
Product Type Classification
After reading products/{product_slug}.json, classify the product based on revenue_model:
- Software product:
revenue_modelissubscriptionorhybrid— features are software capabilities (screens, APIs, automations, integrations). Evaluate with a software demo lens. - Service product:
revenue_modelisproject,project-fee, orpartnership— features are distinct service offerings (methodologies, delivery frameworks, managed processes, certification programs, training curricula). Evaluate with a service delivery lens.
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 · 420 lines · 21 tokens per session scan A 6423245a745d
feature-review-assessor 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 5,328 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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