Auto-Company is a software system that runs a recurring team of AI agents on a personal computer, where the agents plan work, make decisions, write code, deploy applications, and handle marketing. It is intended for autonomous product development on Windows, Linux, or macOS, using command-line agent tools and a local dashboard to track cycles and results. The catalogue entries are skills, agents, settings, and instructions that support this automated workflow.
Borrowing it
Nothing to install: this file belongs to MaxMiksa/Auto-Company. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MaxMiksa/Auto-Company/main/.claude/skills/deep-analysis/SKILL.mdgit clone --depth 1 https://github.com/MaxMiksa/Auto-CompanyWrote 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/maxmiksa/auto-company/deep-analysis)<a href="https://agentmods.dev/skills/maxmiksa/auto-company/deep-analysis"><img src="https://agentmods.dev/badge/skills/maxmiksa/auto-company/deep-analysis/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/maxmiksa/auto-company/deep-analysis"><img src="https://agentmods.dev/badge/skills/maxmiksa/auto-company/deep-analysis.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 | $0.00032 | $0.02870 |
| Opus 5.5 | $0.00013 | $0.01148 |
| Sonnet 5.5 | $0.00006 | $0.00574 |
| Haiku 4.5 | $0.00003 | $0.00287 |
Grade A, and why
deep-analysis 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deep-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Analysis Skill
Comprehensive analytical templates for thorough investigation, audits, and evaluations leveraging extended thinking capabilities.
When to Use
- Code audits requiring systematic review
- Security assessments and threat modeling
- Performance analysis and optimization planning
- Architecture reviews and technical debt assessment
- Incident post-mortems and root cause analysis
- Compliance audits and risk assessments
Analysis Templates
Code Audit Template
## Code Audit Report
**Repository**: [repo-name]
**Scope**: [files/modules audited]
**Date**: [YYYY-MM-DD]
**Auditor**: Claude + [Human reviewer]
### Executive Summary
[2-3 sentence overview of findings]
### Audit Criteria
- [ ] Code quality and maintainability
- [ ] Security vulnerabilities
- [ ] Performance concerns
- [ ] Test coverage
- [ ] Documentation completeness
- [ ] Dependency health
### Critical Findings
| ID | Severity | Location | Issue | Recommendation |
|----|----------|----------|-------|----------------|
| C1 | Critical | file:line | [Issue] | [Fix] |
| C2 | Critical | file:line | [Issue] | [Fix] |
### High Priority Findings
| ID | Severity | Location | Issue | Recommendation |
|----|----------|----------|-------|----------------|
| H1 | High | file:line | [Issue] | [Fix] |
### Medium Priority Findings
[...]
### Low Priority / Suggestions
[...]
### Metrics
| Metric | Value | Target | Status |
|--------|-------|--------|--------|
| Test Coverage | 75% | 80% | ⚠️ |
| Cyclomatic Complexity | 12 | <10 | ⚠️ |
| Technical Debt | 4.2d | <3d | ❌ |
| Security Score | 8/10 | 9/10 | ⚠️ |
### Recommendations
1. **Immediate**: [Critical fixes]
2. **Short-term**: [Within sprint]
3. **Long-term**: [Tech debt reduction]
### Sign-off
- [ ] All critical issues addressed
- [ ] High priority issues have timeline
- [ ] Audit findings documented in backlog
Security Threat Model Template
## Threat Model: [System/Component Name]
**Version**: [1.0]
**Last Updated**: [YYYY-MM-DD]
**Classification**: [Internal/Confidential]
### System Overview
[Brief description of the system being modeled]
### Assets
| Asset | Description | Sensitivity | Owner |
|-------|-------------|-------------|-------|
| User Data | PII, credentials | Critical | Auth Team |
| API Keys | Service credentials | High | DevOps |
| Business Data | Transactions | High | Product |
### Trust Boundaries
┌─────────────────────────────────────────┐ │ External (Untrusted) │ │ [Internet Users] [Third-party APIs] │ └──────────────────┬──────────────────────┘ │ WAF/Load Balancer ┌──────────────────┴──────────────────────┐ │ DMZ (Semi-trusted) │ │ [API Gateway] [CDN] [Public Services] │ └──────────────────┬──────────────────────┘ │ Internal Firewall ┌──────────────────┴──────────────────────┐ │ Internal (Trusted) │ │ [App Servers] [Databases] [Queues] │ └─────────────────────────────────────────┘
### Threat Categories (STRIDE)
#### Spoofing
| Threat | Likelihood | Impact | Mitigation |
|--------|------------|--------|------------|
| Credential theft | Medium | High | MFA, rate limiting |
| Session hijacking | Low | High | Secure cookies, HTTPS |
#### Tampering
| Threat | Likelihood | Impact | Mitigation |
|--------|------------|--------|------------|
| SQL injection | Medium | Critical | Parameterized queries |
| Data modification | Low | High | Integrity checks |
#### Repudiation
[...]
#### Information Disclosure
[...]
#### Denial of Service
[...]
#### Elevation of Privilege
[...]
### Attack Vectors
1. **Vector 1**: [Description]
- Entry point: [Where]
- Technique: [How]
- Mitigation: [Defense]
### Risk Matrix
| Threat | Likelihood | Impact | Risk Score | Priority |
|--------|------------|--------|------------|----------|
| T1 | High | Critical | 9 | P1 |
| T2 | Medium | High | 6 | P2 |
| T3 | Low | Medium | 3 | P3 |
### Security Controls
| Control | Type | Status | Coverage |
|---------|------|--------|----------|
| WAF | Preventive | ✅ Active | External |
| SAST | Detective | ✅ CI/CD | Code |
| DAST | Detective | ⚠️ Partial | Runtime |
| Encryption | Preventive | ✅ Active | Data |
### Recommendations
1. [Priority 1 recommendations]
2. [Priority 2 recommendations]
3. [Priority 3 recommendations]
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 · 395 lines · 32 tokens per session scan A ee116608b705
deep-analysis is a skill published in the GitHub repository MaxMiksa/Auto-Company (3,141 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 2,870 once invoked, about $0.0001 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.
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