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 agents/gpt-cmdr/ras-commander/best-practice-extractorgit clone --depth 1 https://github.com/gpt-cmdr/ras-commanderWrote 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/agents/gpt-cmdr/ras-commander/best-practice-extractor)<a href="https://agentmods.dev/agents/gpt-cmdr/ras-commander/best-practice-extractor"><img src="https://agentmods.dev/badge/agents/gpt-cmdr/ras-commander/best-practice-extractor.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 | $0.00046 | $0.00795 |
| Opus 5 | $0.00023 | $0.00398 |
| Sonnet 5 | $0.00009 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
best-practice-extractor 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 6d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Best Practice Extractor
Extract best practices and successful strategies from conversation history.
Purpose
Analyze conversations to identify:
- Explicit best practice recommendations
- Successful approaches worth repeating
- Patterns worth formalizing
- Lessons learned
Detection Keywords
BEST_PRACTICE_KEYWORDS = [
"best practice", "should always", "always use", "never use",
"recommended", "prefer", "important to", "make sure to",
"don't forget", "remember to", "key is to", "the pattern is",
"rule of thumb", "guideline", "standard", "convention"
]
Analysis Method
1. Explicit Practice Detection
for msg in messages:
for kw in BEST_PRACTICE_KEYWORDS:
if kw in msg.lower():
# Extract context around keyword
practices.append({
'practice': extract_practice(msg, kw),
'context': msg[:500],
'session_id': session_id
})
2. Success Pattern Detection
SUCCESS_INDICATORS = [
"works great", "perfect", "exactly what", "this is the way",
"much better", "finally works", "correct approach"
]
for msg in messages:
if any(ind in msg.lower() for ind in SUCCESS_INDICATORS):
# Extract what was successful
successes.append(analyze_success_context(msg))
3. Categorization
CATEGORIES = {
"code": ["function", "class", "method", "code", "implementation"],
"workflow": ["workflow", "process", "approach", "steps"],
"documentation": ["document", "readme", "comment", "docstring"],
"testing": ["test", "validate", "verify", "check"],
"architecture": ["pattern", "design", "structure", "organization"]
}
Output Format
{
"best_practices": [
{
"category": "code",
"practice": "Use @staticmethod for state-free operations in ras-commander",
"rationale": "Cleaner API, no instantiation needed, consistent with library pattern",
"implementation": "Add @staticmethod decorator, call directly on class",
"session_ids": ["abc123"],
"evidence": ["The static class pattern provides..."]
}
]
}
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.
- 6d ago First seen · 125 lines · 46 tokens per session scan A aa283175c84b
best-practice-extractor is an agent published in the GitHub repository gpt-cmdr/ras-commander (79 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 795 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.