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/matteocervelli/llmsWrote 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/matteocervelli/llms/user-story-annotate)<a href="https://agentmods.dev/commands/matteocervelli/llms/user-story-annotate"><img src="https://agentmods.dev/badge/commands/matteocervelli/llms/user-story-annotate/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/commands/matteocervelli/llms/user-story-annotate"><img src="https://agentmods.dev/badge/commands/matteocervelli/llms/user-story-annotate.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 | $0.00013 | $0.00977 |
| Opus 5.5 | $0.00005 | $0.00391 |
| Sonnet 5.5 | $0.00003 | $0.00195 |
| Haiku 4.5 | $0.00001 | $0.00098 |
Grade A, and why
user-story-annotate 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 1mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Technical Context to User Stories
Add technical annotations, implementation hints, and effort estimates to user stories.
Usage
/user-story-annotate <story-id>
/user-story-annotate --all
Examples
/user-story-annotate US-0001
/user-story-annotate US-0001 US-0002 US-0003
/user-story-annotate --all
What it does
- Analyze Requirements - Reviews story details and acceptance criteria
- Identify Tech Stack - Determines relevant technologies
- Generate Hints - Provides specific implementation guidance
- List Components - Identifies affected system components
- Estimate Effort - Provides realistic time estimates
- Assess Complexity - Rates complexity (low/medium/high)
- Identify Risks - Highlights technical challenges
- Update Story - Adds annotations to YAML file
- Regenerate Docs - Updates Markdown documentation
Result
- Technical context added to story YAML
- Implementation hints for developers
- Component impact analysis
- Effort estimation and complexity rating
- Risk identification
- Updated Markdown documentation
Activate the technical-annotator skill to add technical context to user stories.
Use the technical-annotator skill located in .claude/skills/technical-annotator/SKILL.md.
Parameters
- story-id: One or more story IDs to annotate (e.g., US-0001)
- --all: Annotate all stories in the system
Technical Annotations Include
1. Tech Stack
Technologies needed for implementation:
- Frontend (React, Vue, Angular, TypeScript)
- Backend (FastAPI, Django, Express, Node.js)
- Database (PostgreSQL, MongoDB, Redis)
- Infrastructure (Docker, Kubernetes, AWS)
- Testing (pytest, Jest, Cypress)
2. Implementation Hints
Specific guidance like:
- "Use FastAPI dependency injection for database sessions"
- "Implement OAuth2 password flow with JWT tokens"
- "Create React hook for authentication state"
- "Add database migration for new tables"
3. Affected Components
List of files that will be:
- Created: New files to implement
- Modified: Existing files to change
- Shared: Common utilities or services
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.
- 1mo ago First seen · 138 lines · 13 tokens per session scan A 9a187cc37b9f
user-story-annotate is a command published in the GitHub repository matteocervelli/llms (25 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 977 once invoked, about $0.0001 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 2026-09-03.
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