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 agentmods add skills/jpoutrin/product-forge/pattern-detectionnpx skills add jpoutrin/product-forge --skill pattern-detectiongit clone --depth 1 https://github.com/jpoutrin/product-forgeWhat 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 | $0.00033 | $0.01146 |
| Opus 5 | $0.00016 | $0.00573 |
| Sonnet 5 | $0.00007 | $0.00229 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
pattern-detection 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pattern Detection Skill
Recognize and note reusable patterns during implementation for potential contribution to Product Forge.
Purpose
When working on projects, Claude often implements patterns that could benefit the broader Product Forge ecosystem. This skill helps identify those patterns so they can be captured by the feedback hooks and potentially become new skills, commands, or templates.
Pattern Categories
Code Patterns
Implementations that follow consistent, reusable structures:
- Factory patterns - Test fixtures, mock builders, data generators
- Service layer patterns - Repository, command, query separation
- Error handling - Consistent error types, recovery strategies
- API patterns - Response formatting, pagination, filtering
- Testing patterns - Fixtures, assertions, mocking strategies
Workflow Patterns
Multi-step processes that could be automated:
- Project setup - Directory structures, config files, initial scaffolding
- Code review - Checklists, validation steps, quality gates
- Deployment - Build, test, deploy sequences
- Documentation - Auto-generation, formatting, templates
Configuration Patterns
Settings and configurations that are commonly needed:
- Tool configurations - Linter rules, formatters, CI/CD
- Environment setup - Development, staging, production
- Integration patterns - API keys, service connections
Recognition Triggers
Note patterns when you observe:
- Repetition: Same structure implemented 3+ times
- Best practice: Industry-standard patterns being applied
- Automation opportunity: Manual process that could be scripted
- Convention enforcement: Rules applied manually that could be automated
- Boilerplate reduction: Repeated code that could be templated
Quality Criteria
Only patterns worth capturing should be:
| Criterion | Description |
|---|---|
| Reusable | Applies to multiple projects or contexts |
| Non-trivial | More than simple one-liners or obvious code |
| Generalizable | Not too specific to one codebase |
| Documented | Can be explained clearly to others |
| Tested | Validated in at least one real project |
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 · 164 lines · 33 tokens per session scan A 31bc81ff5c74
pattern-detection is a skill published in the GitHub repository jpoutrin/product-forge (15 stars, last pushed 6mo ago), licensed MIT. It adds 33 tokens to every session and 1,146 once invoked, about $0.0002 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.
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