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.
npx skills add fjpulidop/specrails-core --skill auto-propose-backlog-specsgit clone --depth 1 https://github.com/fjpulidop/specrails-coreWrote 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/skills/fjpulidop/specrails-core/auto-propose-backlog-specs)<a href="https://agentmods.dev/skills/fjpulidop/specrails-core/auto-propose-backlog-specs"><img src="https://agentmods.dev/badge/skills/fjpulidop/specrails-core/auto-propose-backlog-specs/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/skills/fjpulidop/specrails-core/auto-propose-backlog-specs"><img src="https://agentmods.dev/badge/skills/fjpulidop/specrails-core/auto-propose-backlog-specs.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 | $0.00030 | $0.02780 |
| Opus 5 | $0.00015 | $0.01390 |
| Sonnet 5 | $0.00006 | $0.00556 |
| Haiku 4.5 | $0.00003 | $0.00278 |
Grade A, and why
auto-propose-backlog-specs scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s \ How it starts
The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze the project from a product perspective to generate new feature ideas. Syncs results to GitHub Issues labeled product-driven-backlog. Use /specrails:get-backlog-specs to view current ideas.
Input: $ARGUMENTS (optional: comma-separated areas to focus on. If empty, analyze all areas.)
IMPORTANT: This command only creates GitHub Issues. You may read files and search code to understand current capabilities, but you must NEVER write application code.
Areas
Read .specrails/config.yaml or CLAUDE.md to identify project areas/modules. If not defined, use high-level categories inferred from the codebase structure (e.g., Frontend, Backend, API, Data, Infrastructure, Developer Experience).
Execution
Launch a single explorer subagent (subagent_type: Explore, run_in_background: true) for product discovery.
The Explore agent receives this prompt:
You are a product strategist analyzing the this project (read name from CLAUDE.md, package.json, or directory name) to generate new feature ideas using the Value Proposition Canvas framework.
Your goal: For each area, propose 2-4 new features that would significantly improve the user experience. Every feature MUST be evaluated against the project's personas.
Areas to analyze: {all areas or filtered by user input}
Step 0: Read Personas
Before anything else, read all persona files: Read all persona files from
.specrails/personas/*.mdand.claude/agents/personas/*.md. For each persona, extract their Jobs, Pains, and Gains from the Value Proposition Canvas section.These contain full Value Proposition Canvas profiles (jobs, pains, gains).
Research steps
- Understand current capabilities — Read codebase structure
- Check existing backlog — Avoid duplicating existing issues
- Think through each persona's day — For each area:
- What does each persona need here?
- What would a competitive tool offer?
- What data is available but not surfaced?
- For each idea, produce a VPC evaluation:
- Feature name (short, descriptive)
- User story ("As a [user type], I want to [action] so that [benefit]")
- Feature description (2-3 sentences)
- VPC Fit per persona: Jobs, Pains relieved, Gains created, Score (0-5)
- Total Persona Score: sum of all persona scores / max possible
- Effort (High/Medium/Low)
- Inspiration (competitor or product pattern)
- Prerequisites
- Area
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.
- 11d ago First seen · 288 lines · 30 tokens per session scan A f34cc0ff6d20
auto-propose-backlog-specs is a skill published in the GitHub repository fjpulidop/specrails-core (9 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 2,780 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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