Borrowing it
Nothing to install: this file belongs to SylphxAI/pdf-reader-mcp. 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/SylphxAI/pdf-reader-mcp/main/.opencode/command/saas-discovery.mdgit clone --depth 1 https://github.com/SylphxAI/pdf-reader-mcpWrote 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/sylphxai/pdf-reader-mcp/saas-discovery)<a href="https://agentmods.dev/commands/sylphxai/pdf-reader-mcp/saas-discovery"><img src="https://agentmods.dev/badge/commands/sylphxai/pdf-reader-mcp/saas-discovery.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 | $0.00017 | $0.01087 |
| Opus 5 | $0.00009 | $0.00544 |
| Sonnet 5 | $0.00003 | $0.00217 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
saas-discovery 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.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategic Discovery & Opportunities
Scope
Cross-domain strategic exploration: identify new feature opportunities, optimize pricing/packaging, and conduct competitive research. This is NOT compliance checking — it's creative/strategic work to improve the product.
Mandate
- Exploration required: identify improvements for competitiveness, completeness, usability, reliability, and monetization within fixed constraints.
- Think beyond current implementation — what's missing? What would make users love this product?
- Convert insights into testable acceptance criteria, not vague suggestions.
Feature Discovery
Process
- Audit current capabilities: What does the product do today?
- Identify gaps: What's missing that users expect?
- Prioritize by impact: What would move the needle most?
- Define success criteria: How would we know the feature works?
Areas to Explore
- User workflows: Are there manual steps that could be automated?
- Integrations: What third-party services should we connect to?
- Data/insights: What data do we have that users would value seeing?
- Collaboration: Are there multi-user/team features missing?
- Mobile: Is the mobile experience feature-complete or degraded?
- API/Developer: Should there be a public API? Webhooks for users?
- AI/Automation: Where could AI add value without being gimmicky?
Output Format
For each feature opportunity:
**Feature**: [Name]
**Problem**: [What user problem does this solve?]
**Impact**: [High/Medium/Low] — [Why?]
**Effort**: [High/Medium/Low] — [Why?]
**Success Criteria**: [How do we measure success?]
**Acceptance Criteria**:
- [ ] [Specific, testable requirement]
- [ ] [Another requirement]
Pricing & Monetization Discovery
Process
- Audit current pricing: Tiers, features per tier, price points
- Analyze value alignment: Does pricing match perceived value?
- Identify friction: Where do users hesitate to pay?
- Explore models: Subscription, usage-based, hybrid, freemium
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.
- 2d ago First seen · 136 lines · 17 tokens per session scan A 3a18e324d41e
saas-discovery is a command published in the GitHub repository SylphxAI/pdf-reader-mcp (919 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 1,087 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 2026-09-06.
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