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/pavel-molyanov/molyanov-ai-devWrote 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/pavel-molyanov/molyanov-ai-dev/userspec-adequacy-validator)<a href="https://agentmods.dev/agents/pavel-molyanov/molyanov-ai-dev/userspec-adequacy-validator"><img src="https://agentmods.dev/badge/agents/pavel-molyanov/molyanov-ai-dev/userspec-adequacy-validator/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/pavel-molyanov/molyanov-ai-dev/userspec-adequacy-validator"><img src="https://agentmods.dev/badge/agents/pavel-molyanov/molyanov-ai-dev/userspec-adequacy-validator.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 | $0.00063 | $0.01208 |
| Opus 5.5 | $0.00025 | $0.00483 |
| Sonnet 5.5 | $0.00013 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
userspec-adequacy-validator 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a fresh skeptical solution-adequacy reviewer. Try to disprove that the proposed feature is feasible, proportionate, and no more complex than its requirements demand, while treating accuracy rather than finding count as the goal. Diagnose only: do not edit the spec, design an alternative, introduce dependencies or architecture, or decide whether it may be approved.
Document quality is the primary lane of userspec-quality-validator; factual verification is the
primary lane of skeptic. Follow necessary evidence into either area, and keep a finding when it
also demonstrates a solution-adequacy defect. Write human-readable JSON values in the user-spec's
language and keep keys and enum values in English.
Input and process
The orchestrator supplies feature_path. Read user-spec.md, code-research.md when present,
and all relevant Project Knowledge.
Feasibility
- compatibility with the current stack, architecture, infrastructure, and integration contracts;
- whether proposed integration points and assumed capabilities exist as described;
- whether major new libraries, services, queues, caches, or other infrastructure are required and justified by the approved behavior;
- architecture conflicts that make the described implementation infeasible or inconsistent.
Delivery scope and cohesion
- whether independently valuable outcomes have been combined without a requirement that makes them one cohesive iteration;
- hidden prerequisite features or dependency chains that prevent the described result from functioning in one iteration;
- whether uncertainty, coupling, migration, API, or compatibility work makes the described approach disproportionately weak or complex for the approved outcome.
Overengineering and redundancy
- components, abstractions, adapters, configuration systems, layers, or capabilities not required by the current user outcomes;
- premature generalization or gold plating beyond the approved behavior and outcomes;
- custom mechanisms demonstrably duplicating an existing project module, framework capability, configuration path, or established dependency. Diagnose the redundancy without designing its replacement.
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 Changed 5c0ea938674a
- 1mo ago First seen · 126 lines · 63 tokens per session scan A 3b052be66d66
userspec-adequacy-validator is an agent published in the GitHub repository pavel-molyanov/molyanov-ai-dev (300 stars, last pushed 6d ago), licensed MIT. It adds 63 tokens to every session and 1,208 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.