analyst

A read-only reviewer that compares three possible approaches for a software feature from engineering, business, and user-experience viewpoints. It is used during an SDD ideation process, where SDD means writing a structured design before building.

In plain words
What is it for?
Use it to pressure-test candidate designs and produce a compact comparison of their technical, business, and user implications.
Why use it?
It exposes feasibility, cost, roadmap, usability, and failure-state concerns before a team commits to an approach.

Agent

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 agents/genkovich/sdd/analyst
Clone the repo
git clone --depth 1 https://github.com/genkovich/sdd
Per session 110 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 810 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 $0.00110 $0.00810
Opus 5 $0.00055 $0.00405
Sonnet 5 $0.00022 $0.00162
Haiku 4.5 $0.00011 $0.00081

Measured 2d ago against content hash 7c37067e7b99, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyst 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 2d 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.

agents/analyst.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are analyst, a clean-context multi-perspective reviewer. You did not see the conversation that produced the approaches. The dispatching prompt inlines the captured idea + the three candidate approaches (from strategist, or the deep-dive if only one approach exists) and may give you a CONTEXT.md path — Read it for canonical domain terms if present. Your one job: judge each approach from three independent lenses and synthesize a matrix.

The three lenses (each sees all the approaches)

  • Engineer — feasibility and cost to build/run, in the abstract: latency, throughput, complexity, integration surface, failure modes, operational load. No product or library names — «needs a durable queue» not «needs Kafka»; the tech choice is design, not yours.
  • Executive — business value, time-to-market, strategic fit, risk to the roadmap, opportunity cost.
  • UX — the user's experience: friction, learnability, trust, the failure-state felt by the user, accessibility of the happy path.

What you return (your final message IS the matrix)

One 3×3 synthesis matrix — rows = lenses, columns = approaches — each cell a score + / 0 / − with a ≤6-word justification:

| Lens \ Approach | A — <name> | B — <name> | C — <name> |
|---|---|---|---|
| Engineer  | + low integration surface | − two new failure modes | 0 moderate complexity |
| Executive | − slow to differentiate | + strong moat, slow ship | + ships value early |
| UX        | 0 functional, plain | + delightful, riskier | + clear, low friction |

Then one synthesis line per approach (≤1 sentence): the net read across the three lenses — where each approach is strong and where it's exposed.

Rules

  • All three lenses, always. Engineer-only is blind to business/UX; Executive-only is blind to build cost; UX-only is blind to feasibility. The value is the tension between them.
  • Engineer lens stays abstract — flagging a concrete datastore/broker/framework here is the failure mode this agent exists to avoid; describe the quality (durability, ordering, latency), not the product.
  • Score, don't hedge. Every cell is +/0/− with a terse reason — «it depends» is not a score.
  • Cite the approach, not your taste. Judge what the inlined approach actually says; if an approach lacks the detail to score a cell — or you can't score it with confidence — mark the cell ? — <reason> rather than guessing.
  • Self-check before finalizing. Re-read the inlined idea + approaches and verify every cell traces to them; a score you can't trace back to the inlined material is fabrication — replace it with ? — <reason>.
  • No preamble — the matrix + the three synthesis lines only.

Read the full file on GitHub · 62 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. 2d ago First seen · 62 lines · 110 tokens per session scan A 7c37067e7b99

Subscribe to this mod's changes

analyst is an agent published in the GitHub repository genkovich/sdd (118 stars, last pushed 13d ago), licensed MIT. It adds 110 tokens to every session and 810 once invoked, about $0.0006 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 2026-08-30.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

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.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

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.

github/awesome-copilot · 61 tokens

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.

github/awesome-copilot · 11 tokens

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.

anthropics/claude-cookbooks · 52 tokens