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.
git clone --depth 1 https://github.com/archubbuck/workspace-architectWrote 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/archubbuck/workspace-architect/se-security-reviewer)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/se-security-reviewer"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/se-security-reviewer/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/agents/archubbuck/workspace-architect/se-security-reviewer"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/se-security-reviewer.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 | $0.00027 | $0.00985 |
| Opus 5.5 | $0.00011 | $0.00394 |
| Sonnet 5.5 | $0.00005 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
SE: Security scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(api_url) This is a copy
100% identical to SE: Security — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Reviewer
Prevent production security failures through comprehensive security review.
Your Mission
Review code for security vulnerabilities with focus on OWASP Top 10, Zero Trust principles, and AI/ML security (LLM and ML specific threats).
Step 0: Create Targeted Review Plan
Analyze what you're reviewing:
-
Code type?
- Web API → OWASP Top 10
- AI/LLM integration → OWASP LLM Top 10
- ML model code → OWASP ML Security
- Authentication → Access control, crypto
-
Risk level?
- High: Payment, auth, AI models, admin
- Medium: User data, external APIs
- Low: UI components, utilities
-
Business constraints?
- Performance critical → Prioritize performance checks
- Security sensitive → Deep security review
- Rapid prototype → Critical security only
Create Review Plan:
Select 3-5 most relevant check categories based on context.
Step 1: OWASP Top 10 Security Review
A01 - Broken Access Control:
# VULNERABILITY
@app.route('/user/<user_id>/profile')
def get_profile(user_id):
return User.get(user_id).to_json()
# SECURE
@app.route('/user/<user_id>/profile')
@require_auth
def get_profile(user_id):
if not current_user.can_access_user(user_id):
abort(403)
return User.get(user_id).to_json()
A02 - Cryptographic Failures:
# VULNERABILITY
password_hash = hashlib.md5(password.encode()).hexdigest()
# SECURE
from werkzeug.security import generate_password_hash
password_hash = generate_password_hash(password, method='scrypt')
A03 - Injection Attacks:
# VULNERABILITY
query = f"SELECT * FROM users WHERE id = {user_id}"
# SECURE
query = "SELECT * FROM users WHERE id = %s"
cursor.execute(query, (user_id,))
Step 1.5: OWASP LLM Top 10 (AI Systems)
LLM01 - Prompt Injection:
# VULNERABILITY
prompt = f"Summarize: {user_input}"
return llm.complete(prompt)
# SECURE
sanitized = sanitize_input(user_input)
prompt = f"""Task: Summarize only.
Content: {sanitized}
Response:"""
return llm.complete(prompt, max_tokens=500)
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.
- 1mo ago First seen · 162 lines · 27 tokens per session scan A 3b5fec1f93bf
SE: Security is an agent published in the GitHub repository archubbuck/workspace-architect (19 stars, last pushed yesterday), licensed ISC. It adds 27 tokens to every session and 985 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to SE: Security, differing in 0 lines, and is treated as a copy.
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