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/dnyoussef/context-cascade/audit-pipeline-guidegit clone --depth 1 https://github.com/DNYoussef/context-cascadeWrote 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/dnyoussef/context-cascade/audit-pipeline-guide)<a href="https://agentmods.dev/agents/dnyoussef/context-cascade/audit-pipeline-guide"><img src="https://agentmods.dev/badge/agents/dnyoussef/context-cascade/audit-pipeline-guide.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 | $0.00000 | $0.04991 |
| Opus 5 | $0.00000 | $0.02495 |
| Sonnet 5 | $0.00000 | $0.00998 |
| Haiku 4.5 | $0.00000 | $0.00499 |
Grade A, and why
audit-pipeline-guide 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 5d 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 — 838 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Pipeline - Complete Guide
Overview
The Audit Pipeline is a comprehensive 3-phase code quality orchestrator that systematically transforms prototype code into production-ready software through:
- Theater Detection - Identifies and eliminates mock/fake/placeholder code
- Functionality Audit - Validates code works through sandbox testing with Codex iteration
- Style & Quality Audit - Polishes code to professional production standards
Why This Pipeline Exists
Code goes through stages:
- Prototype: "Make it work" - lots of shortcuts, mocks, TODOs
- Functional: "Make it right" - real implementations, tests passing
- Production: "Make it excellent" - clean, documented, maintainable
The Audit Pipeline automates this transformation.
The 3-Phase System
Phase 1: Theater Detection Audit
Purpose: Find all code that looks like it works but is actually fake.
What It Finds:
- Hardcoded mock data (
return {"id": 123, "name": "Test User"}) - TODO/FIXME markers indicating incomplete work
- Stub functions that exist but don't do anything
- Commented-out production code
- Simplified error handling that always succeeds
- Test mode conditionals that bypass real logic
Process:
- Pattern-based scanning for theater indicators
- Contextual analysis to understand intended behavior
- Dependency mapping between theater instances
- Risk assessment (critical vs. minor)
- Completion or documentation
Output: Theater audit report listing all instances with locations, severity, and completion status.
Skill Used: theater-detection-audit
Phase 2: Functionality Audit with Codex Sandbox
Purpose: Prove code actually works through execution testing.
What It Does:
- Creates isolated sandbox environment
- Generates comprehensive test cases
- Executes code with realistic inputs
- Verifies outputs match expectations
- Uses Codex to fix failures automatically
The Codex Integration Loop:
For each failing test:
┌─────────────────────────────────────┐
│ 1. Capture test failure details │
│ - Error message │
│ - File and line │
│ - Code context │
└──────────┬──────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ 2. Spawn Codex in sandbox │
│ codex --full-auto "Fix test..." │
│ - Network disabled (secure) │
│ - CWD only (isolated) │
│ - Autonomous execution │
└──────────┬──────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ 3. Codex analyzes and fixes │
│ - Reads code │
│ - Identifies root cause │
│ - Implements fix │
│ - Runs tests │
└──────────┬──────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ 4. Re-test in sandbox │
│ - Run the specific test │
│ - Check if passing │
└──────────┬──────────────────────────┘
│
┌────┴────┐
│ Passing? │
└────┬────┘
YES │ NO (iteration < 5)
│ │
│ └──► Repeat with more context
│
▼
┌─────────────────────────────────────┐
│ 5. Validate no regressions │
│ - Run full test suite │
│ - Ensure other tests still pass │
└──────────┬──────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ 6. Apply fix to main codebase │
│ - Copy changes from sandbox │
│ - Document what was fixed │
│ - Track in audit report │
└─────────────────────────────────────┘
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.
- 5d ago First seen · 838 lines · 0 tokens per session scan A 5d2d0f9de5e4
audit-pipeline-guide is an agent published in the GitHub repository DNYoussef/context-cascade (33 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,991 tokens. 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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