Borrowing it
Nothing to install: this file belongs to Kandil7/prprompts-flutter-generator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Kandil7/prprompts-flutter-generator/master/.claude/commands/prd/analyze.mdgit clone --depth 1 https://github.com/Kandil7/prprompts-flutter-generatorWrote 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/commands/kandil7/prprompts-flutter-generator/analyze)<a href="https://agentmods.dev/commands/kandil7/prprompts-flutter-generator/analyze"><img src="https://agentmods.dev/badge/commands/kandil7/prprompts-flutter-generator/analyze.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.03207 |
| Opus 5 | $0.00008 | $0.01604 |
| Sonnet 5 | $0.00003 | $0.00641 |
| Haiku 4.5 | $0.00002 | $0.00321 |
Grade A, and why
Analyze PRD 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 — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Product Requirements Document
Overview
Validate PRD structure, calculate comprehensive quality scores (Completeness, Clarity, Feasibility, Security), assign overall grade (A-F), and show what PRPROMPTS customizations will be applied based on the PRD metadata.
Input File
Look for: docs/PRD.md
Analysis Steps
Step 1: Validate YAML Frontmatter
Check for required fields:
project_name(required)project_type(required)platforms(required, must have at least one)auth_method(required)
Step 2: Calculate Quality Scores
2.1 Completeness Score (0-100%)
Section Presence (60% weight): Count present sections out of 15 expected:
- Executive Summary
- Product Vision
- Target Users (with personas)
- Core Features (detailed)
- Non-Functional Requirements
- Compliance Requirements (if compliance != [])
- User Flows
- Data Model
- API Specifications
- Design Guidelines
- Technical Architecture
- Testing Strategy
- Deployment Plan
- Timeline & Milestones
- Success Metrics
YAML Completeness (20% weight):
- All required fields present: project_name, project_type, platforms, auth_method
- Optional but important fields: features, team_composition, testing_requirements
Feature Detail (20% weight): For each feature, check:
- Has description (2 points)
- Has user stories (3 points)
- Has acceptance criteria (3 points)
- Has technical requirements (2 points)
- Total per feature: 10 points
- Average across all features
Formula:
Completeness = (Present_Sections/15 × 60) + (YAML_Fields × 20) + (Avg_Feature_Detail × 20)
2.2 Clarity Score (0-100%)
Ambiguity Detection (40% weight): Scan for vague language in critical sections:
- "maybe", "possibly", "might", "could be", "approximately", "around"
- "TBD", "TODO", "To be determined"
- "probably", "likely", "hopefully"
- Count ambiguous phrases / total sentences
- Clarity_Sub1 = 100 - (ambiguous_count × 5) # max deduction 100
Measurable Criteria (30% weight):
- Acceptance criteria are quantified (not just "fast", but "< 2 seconds")
- Success metrics follow SMART format
- Performance targets have numbers
- Timeline has specific dates
- Clarity_Sub2 = (Measurable_criteria / Total_criteria) × 100
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 · 373 lines · 16 tokens per session scan A 64b4e14765a0
Analyze PRD is a command published in the GitHub repository Kandil7/prprompts-flutter-generator (11 stars, last pushed 9mo ago), licensed MIT. It adds 16 tokens to every session and 3,207 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 2026-08-30.
Other commands, from other repositories
speckit.bug.fetch
Load an existing GitHub issue into the bug workflow (the complement of bug.issue) and seed a triage draft.
speckit.chore.fetch
Load an existing GitHub issue into the chore workflow and seed a triage draft.
speckit.chore.implement
Implement the scoped chore (the actual maintenance work) and record what changed.
speckit.bug.pr
Open a pull request for the applied bug fix, linking the tracked issue.
speckit.gh-triage.feature
Create a GitHub issue describing a new feature, label it with the configured feature label, and optionally auto-run speckit.specify to turn it into a spec.
speckit.md-doctor.drift
Re-evaluate since the last run: compute the git/.memsearch/TDD delta, re-score previously graded files in light of what changed, and report which past suggestions were resolved or are now stale/false.