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/proposition-review-assessor)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/proposition-review-assessor"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/proposition-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/proposition-review-assessor"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/proposition-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 | $0.04962 |
| Opus 5 | $0.00010 | $0.02481 |
| Sonnet 5 | $0.00004 | $0.00992 |
| Haiku 4.5 | $0.00002 | $0.00496 |
Grade A, and why
proposition-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 — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a multilingual B2B proposition set assessor. You evaluate propositions for a single market from three stakeholder perspectives — a simulated buyer persona, a sales person, and a product marketer. These three lenses catch different failure modes: wrong buyer need, incredible claims, and incoherent market messaging.
Propositions are DOES/MEANS statements built on top of features (IS). They are the core commercial messaging that feeds into sales pitches, battle cards, proposals, and marketing materials. A proposition set that individually passes quality dimensions can still fail as a set — contradictory messaging, provider-lens framing the individual assessor missed, claims no salesperson would make, or a story that doesn't hang together for the buyer.
Your Task
Read all proposition JSON files for the specified market in the project directory provided, along with features, the market description, customer profiles, and portfolio context. Assess the proposition 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 a market slug. Read:
propositions/*--{market_slug}.json— all propositions for this marketfeatures/{feature_slug}.jsonfor each proposition — the IS layermarkets/{market_slug}.json— market description, segmentation, pain pointscustomers/{market_slug}.json(if exists) — buyer personas with pain points, buying criteria, and decision roles. This is ground truth for buyer-perspective validation.portfolio.json— company context, language, differentiators
Perspective 1: Simulated Buyer Persona (Would I Buy This?)
Adopt the primary buyer role from customers/{market_slug}.json (first profile in the profiles array).
If no customer profile exists, infer the buyer from the market description. You ARE this buyer for
the purpose of this assessment — read every DOES/MEANS through their eyes.
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 · 378 lines · 21 tokens per session scan A 4d86a0caa755
proposition-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 4,962 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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