analyze

analyze is a command for coding agents from MostAshraf/ai-sdlc-harness. It costs 0 tokens per session (649 once invoked), scanned A, original, MIT.

A command that checks whether a work item, such as a user story or task, is ready for development. It produces a report with specific problems and suggested improvements without changing the item.

In plain words
What is it for?
It is for reviewing a story's description, acceptance criteria, scope, and other quality requirements before refinement.
Why use it?
It finds missing details and unclear or untestable requirements before the team spends time refining or building the work.

Command

Part of the ai-sdlc-harness plugin — 8 skills, 4 commands, 3 agents, 4 hooks shipped together

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/mostashraf/ai-sdlc-harness/analyze
Clone the repo
git clone --depth 1 https://github.com/MostAshraf/ai-sdlc-harness

Or install ai-sdlc-harness, the plugin that ships this one along with the rest of its 8 skills, 4 commands, 3 agents, 4 hooks.

Wrote 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.

agentmods badge for analyze

README.md
[![agentmods](https://agentmods.dev/badge/commands/mostashraf/ai-sdlc-harness/analyze.svg)](https://agentmods.dev/commands/mostashraf/ai-sdlc-harness/analyze)
Your own site
<a href="https://agentmods.dev/commands/mostashraf/ai-sdlc-harness/analyze"><img src="https://agentmods.dev/badge/commands/mostashraf/ai-sdlc-harness/analyze.svg" alt="Measured on agentmods" height="20"></a>
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 649 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 5.1 $0.00000 $0.00649
Opus 5 $0.00000 $0.00324
Sonnet 5 $0.00000 $0.00130
Haiku 4.5 $0.00000 $0.00065

Measured 5d ago against content hash a5a4f8aba04f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

analyze 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 5d 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.

skills/story-workflow/commands/analyze.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/story-workflow analyze

Pre-refinement readiness check. Evaluates a work item against quality criteria and produces a readiness report with actionable flags. Standalone and non-destructive — it never rewrites the story.

For most sessions improve is the better choice — it folds readiness analysis into refinement in one adaptive pass. Reach for analyze when you want a standalone report to share with the PO before touching the story.

Steps

  1. Fetch the work item per shared/provider-io.md. Extract title, type, state, description, and acceptance criteria. If it isn't found, stop.
  2. Domain context (optional): skim shared/context.md only if you need the repo landscape to judge whether the story's scope is realistic.
  3. Evaluate the story against every dimension in templates/readiness-report.md. For each, assign a 🔴/🟡/🟢 flag with a specific explanation. Rules:
    • Be specific, not generic. Not "ACs could be improved" but "AC #2 says 'handles errors gracefully' — untestable; suggest 'Given an invalid token, When login is attempted, Then a 401 with code AUTH_INVALID_TOKEN is returned'."
    • UI ACs must name observable DOM semantics. "the Next button is disabled" reads two defensible ways — rendered carrying disabled, or not rendered at all — and each yields incompatible code AND incompatible tests. Flag it and draft the specific one (field: two independent runs of one story read exactly that AC in opposite directions and shipped mutually incompatible work, each internally consistent). Same for "shows an error", "the row is highlighted", "the field is cleared".
    • Suggest, don't only critique. Every 🔴/🟡 carries a concrete fix — draft the replacement text where you can.
    • Constructive tone. The PO and team will read this.
  4. Write the report in the templates/readiness-report.md format: flags with explanations, suggested improvements (draft Given/When/Then ACs where ACs are missing or vague), and a 2–3 sentence summary assessment.
  5. Present the full report in the conversation, then ask whether to post it as a comment on the item. On yes, post it per shared/provider-io.md. For local-markdown (or any provider), the report is a comment — never overwrite the story with it; if the user wants it persisted separately, offer a sibling file (e.g. <id>-readiness.md) via the Write tool.

Read the full file on GitHub · 48 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. 5d ago First seen · 48 lines · 0 tokens per session scan A a5a4f8aba04f

Subscribe to this mod's changes

analyze is a command published in the GitHub repository MostAshraf/ai-sdlc-harness (18 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 649 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.