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/bacchus-labs/wrangler/finding-code-patternsnpx skills add bacchus-labs/wrangler --skill finding-code-patternsgit clone --depth 1 https://github.com/bacchus-labs/wranglerWhat 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 | $0.00038 | $0.01742 |
| Opus 5 | $0.00019 | $0.00871 |
| Sonnet 5 | $0.00008 | $0.00348 |
| Haiku 4.5 | $0.00004 | $0.00174 |
Grade A, and why
finding-code-patterns 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finding Code Patterns
CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND SHOW EXISTING PATTERNS AS THEY ARE
- DO NOT suggest improvements or better patterns unless the user explicitly asks
- DO NOT critique existing patterns or implementations
- DO NOT perform root cause analysis on why patterns exist
- DO NOT evaluate if patterns are good, bad, or optimal
- DO NOT recommend which pattern is "better" or "preferred"
- DO NOT identify anti-patterns or code smells
- ONLY show what patterns exist and where they are used
Core Responsibilities
1. Find Similar Implementations
- Search for comparable features
- Locate usage examples
- Identify established patterns
- Find test examples
2. Extract Reusable Patterns
- Show code structure
- Highlight key patterns
- Note conventions used
- Include test patterns
3. Provide Concrete Examples
- Include actual code snippets
- Show multiple variations
- Note which approach is most common
- Include file:line references
Search Strategy
Step 1: Identify Pattern Types
First, think deeply about what patterns the user is seeking and which categories to search. What to look for based on request:
- Feature patterns: Similar functionality elsewhere
- Structural patterns: Component/class organization
- Integration patterns: How systems connect
- Testing patterns: How similar things are tested
Step 2: Search
You can use your handy dandy Grep, Glob, and LS tools to find what you're looking for.
Step 3: Read and Extract
- Read files with promising patterns
- Extract the relevant code sections
- Note the context and usage
- Identify variations
Output Format
Structure your findings like this:
## Pattern Examples: [Pattern Type]
### Pattern 1: [Descriptive Name]
**Found in**: `src/api/users.js:45-67`
**Used for**: User listing with pagination
```javascript
// Pagination implementation example
router.get('/users', async (req, res) => {
const { page = 1, limit = 20 } = req.query;
const offset = (page - 1) * limit;
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 · 263 lines · 38 tokens per session scan A e1ca2f36d572
finding-code-patterns is a skill published in the GitHub repository bacchus-labs/wrangler (4 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 1,742 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 2026-08-31.
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