dev-assess

A review command that asks three senior developer agents to assess a feature, module, architecture, or specification from different engineering viewpoints. It combines their findings into an interactive HTML report showing where they agree or disagree.

In plain words
What is it for?
Use it to assess code, design documents, architectures, tests, migrations, or other technical material. It helps identify risks, weaknesses, and areas needing further review.
Why use it?
It gives you several independent technical opinions instead of relying on one review. The report makes shared concerns and conflicting conclusions easier to investigate.

Command

Install

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.

agentmods
npx agentmods add commands/eddiebelaval/squire/dev-assess
Clone the repo
git clone --depth 1 https://github.com/eddiebelaval/squire
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,946 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.02946
Opus 5 $0.00000 $0.01473
Sonnet 5 $0.00000 $0.00589
Haiku 4.5 $0.00000 $0.00295

Measured 3d ago against content hash 87e02462c2e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dev-assess 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 3d 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.

commands/dev-assess.md · 238 lines

How it starts

The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/dev-assess — Senior Dev Triangulated Assessment

Spawn 3 senior developer subagents in parallel, each assessing the target from a different engineering perspective. Experts are dynamically selected based on the target material's technical domains. Triangulate their findings into an interactive HTML artifact with convergence/divergence analysis.

Arguments

  • $ARGUMENTS: The target to assess (e.g., "shadow-engine", "auth-flow", "billing-system"). Can be a feature name, module, architecture, or spec document.

Instructions

Phase 1: Identify the Target

Determine what's being assessed. This could be:

  • A feature or module in a codebase (read the relevant files)
  • A spec document or set of documents (read them)
  • An architecture or design (explore the codebase)
  • A blueprint artifact (parse its embedded JSON)

IMPORTANT: Before spawning experts, YOU must read and understand the target thoroughly. Gather all relevant source material — code files, spec docs, config, tests, migrations. The experts need this context injected into their prompts.

Phase 2: Gather Context

  1. Read all relevant files for the target
  2. Identify the project and directory (e.g., ~/Development/id8/products/parallax)
  3. Build a context summary that includes:
    • What the target does / is supposed to do
    • Key files and their locations
    • Architecture decisions made
    • Dependencies and integrations
    • Any spec documents or design docs

Phase 3: Dynamic Expert Selection

Do NOT default to the same 3 experts every time. After gathering context, analyze the target material to select the 3 most relevant engineering expert perspectives.

Selection Process:

  1. Identify the top 3-5 technical domains the target material touches (e.g., API design, database schema, AI/ML pipeline, frontend UX, security/encryption, real-time systems, deployment/infra, performance, testing strategy, billing/payments, data compliance)
  2. From the expert pool below, select 3 experts that:
    • Maximize coverage of the identified domains
    • Each brings at least one unique analytical framework or methodology not shared by the others
    • Have meaningful pairwise overlap (triangulation requires shared ground to find convergence/divergence)
    • Together cover the full risk surface of the material
  3. For each selected expert, generate a custom profile:
    • Full role description and expertise
    • Frameworks and methodologies in their scope (specific to this assessment)
    • Focus areas tailored to this target
    • Overlap zones with the other 2 selected experts
    • 3-4 key questions they would ask about this specific material

Read the full file on GitHub · 238 lines

Changes

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.

  1. 3d ago First seen · 238 lines · 0 tokens per session scan A 87e02462c2e1

Subscribe to this mod's changes

dev-assess is a command published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,946 tokens. 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.

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