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/fall-out-bug/sdp/realitynpx skills add fall-out-bug/sdp --skill realitygit clone --depth 1 https://github.com/fall-out-bug/sdpWhat 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 | $0.00015 | $0.01150 |
| Opus 5 | $0.00008 | $0.00575 |
| Sonnet 5 | $0.00003 | $0.00230 |
| Haiku 4.5 | $0.00002 | $0.00115 |
Grade A, and why
reality 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@reality - Codebase Analysis & Architecture Validation
Analyze what's actually in your codebase (vs. what's documented).
Workflow
When user invokes @reality:
- Auto-detect project type
- Run scan based on mode (--quick, --deep, --focus)
- Spawn expert agents in parallel using Task tool
- Synthesize report with health score
Step 0: Auto-Detect Project Type
# Detect language/framework
if [ -f "go.mod" ]; then PROJECT_TYPE="go"
elif [ -f "pyproject.toml" ] || [ -f "requirements.txt" ]; then PROJECT_TYPE="python"
elif [ -f "pom.xml" ] || [ -f "build.gradle" ]; then PROJECT_TYPE="java"
elif [ -f "package.json" ]; then PROJECT_TYPE="nodejs"
else PROJECT_TYPE="unknown"
fi
Step 1: Quick Scan (--quick mode)
Analysis:
- Project size (lines of code, file count)
- Architecture (layer violations, circular dependencies)
- Test coverage (if tests exist, estimate %)
- Documentation (doc coverage, drift detection)
- Quick smell check (TODO/FIXME/HACK comments, long files)
Output: Health Score X/100 + Top 5 Issues
Step 2: Deep Analysis (--deep mode)
Spawn 8 parallel expert analyses using Task tool with subagent_type:
Task(subagent_type="general-purpose", prompt="Analyze ARCHITECTURE...")
Task(subagent_type="general-purpose", prompt="Analyze CODE QUALITY...")
Task(subagent_type="general-purpose", prompt="Analyze TESTING...")
Task(subagent_type="general-purpose", prompt="Analyze SECURITY...")
Task(subagent_type="general-purpose", prompt="Analyze PERFORMANCE...")
Task(subagent_type="general-purpose", prompt="Analyze DOCUMENTATION...")
Task(subagent_type="general-purpose", prompt="Analyze TECHNICAL DEBT...")
Task(subagent_type="general-purpose", prompt="Analyze STANDARDS...")
Expert agents:
- ARCHITECTURE expert - Layer mapping, dependencies, violations
- CODE QUALITY expert - File size, complexity, duplication
- TESTING expert - Coverage, test quality, frameworks
- SECURITY expert - Secrets, OWASP, dependencies
- PERFORMANCE expert - Bottlenecks, caching, scalability
- DOCUMENTATION expert - Coverage, drift, quality
- TECHNICAL DEBT expert - TODO/FIXME, code smells
- STANDARDS expert - Conventions, error handling, types
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 · 160 lines · 15 tokens per session scan A 23d58047c382
reality is a skill published in the GitHub repository fall-out-bug/sdp (19 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 1,150 once invoked, about $0.0001 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 skills, from other repositories
typescript-best-practices
Enforces TypeScript best practices and modern patterns.
Tech Debt Auditor
Identifies and prioritizes technical debt in a codebase with an effort/impact matrix.
Unit Test Improver
Reviews existing unit tests for gaps, weak assertions, and missing edge cases, then rewrites them to be more robust.
Refactor Planner
Creates a safe, step-by-step plan to refactor messy code without breaking existing behavior.
code-discipline
Coding methodology for production-grade software development. Enforces structured thinking before coding, verifying reality, simplicity, surgical changes, contract awareness, and verifiable success criteria. Use when writing, reviewing, or refactoring code that needs to remain reliable over time, including production…
umbra-trust-review
Verify AI-generated code before shipping. Run Umbra's Trust Score scan before committing or finishing any coding task, treat findings as blocking issues, and re-scan until clean. Use when finishing a task, before a commit, or when reviewing code written by an agent.