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 lisbeth718/pseo-skills --skill pseo-discoverygit clone --depth 1 https://github.com/lisbeth718/pseo-skillsWrote 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/lisbeth718/pseo-skills/pseo-discovery)<a href="https://agentmods.dev/skills/lisbeth718/pseo-skills/pseo-discovery"><img src="https://agentmods.dev/badge/skills/lisbeth718/pseo-skills/pseo-discovery/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/lisbeth718/pseo-skills/pseo-discovery"><img src="https://agentmods.dev/badge/skills/lisbeth718/pseo-skills/pseo-discovery.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 | $0.00080 | $0.03007 |
| Opus 5.5 | $0.00032 | $0.01203 |
| Sonnet 5.5 | $0.00016 | $0.00601 |
| Haiku 4.5 | $0.00008 | $0.00301 |
Grade A, and why
pseo-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 9d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pSEO Discovery
Analyze the codebase, business context, and market to determine what programmatic SEO pages can and should be built. This skill answers the question: "What should we generate, and do we have the data to do it?"
This skill runs BEFORE pseo-audit. The audit checks if the codebase is ready; this skill figures out what to build.
Core Principles
- Data-first: Only propose page types backed by structured data that exists or can be sourced
- Intent-matched: Every proposed page type must target a real search intent with volume
- Differentiated: Each page must be able to produce genuinely unique content - not just variable swaps
- Feasible: Proposals must be realistic given the current data, codebase, and team capacity
- Business-aligned: pSEO pages should serve the business's actual audience and goals
Discovery Procedure
1. Explore the Codebase for Data Assets
Search the codebase for existing structured data that could power pages:
Database/ORM models:
- Search for schema definitions (Prisma, Drizzle, TypeORM, Mongoose, SQL migrations)
- Identify entities with many records (products, locations, services, users, listings, articles)
- Note which entities have rich attributes (descriptions, categories, images, metadata)
CMS content types:
- Check for headless CMS configs (Contentful, Sanity, Strapi, etc.)
- Identify content types and their field schemas
- Count records per content type
API endpoints:
- Map all API routes and their response shapes
- Identify list endpoints that return collections of entities
- Check for paginated endpoints (signals large datasets)
Static data files:
- Search for JSON, CSV, YAML, MDX files in
data/,content/,src/data/directories - Count records and inspect field richness
- Check for categorization or taxonomy structures
What to extract per data source:
- Entity name and count (e.g., "2,500 products", "150 cities", "80 services")
- Available fields (especially: name, description, category, attributes, images)
- Relationships between entities (product → category, service → location)
- Data freshness (how often updated, is there a lastModified field)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago Changed · -5 lines 70c480ee3855
- 26d ago Changed · +19 lines 389ef3be8a50
- 2mo ago First seen · 249 lines · 80 tokens per session scan A c8f5e10b74b2
pseo-discovery is a skill published in the GitHub repository lisbeth718/pseo-skills (55 stars, last pushed 9d ago), licensed MIT. It adds 80 tokens to every session and 3,007 once invoked, about $0.0003 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 2026-08-30.
pseo-discovery compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| pseo-discoverylisbeth718/pseo-skills | Skill | 55 | 9d ago | MIT | A | 80 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |