dark-code-audit

name	dark-code-audit
description	Assesses structural and velocity dark-code risk from architecture, AI tool usage, ownership, and deployment practices. Produces a direct risk assessment without inventing missing facts.
contract_version	1.0.0
side_effect_profile	read_only
activation	{"mode":"overlay","consumes_manifest":true,"trigger_fields":["service_boundary_change","external_integration","persistent_storage","runtime_path_change","auth_change"],"produces":["dark_code_risk_assessment"]}

Dark Code Audit

Purpose

Dark code is code or runtime behavior that no human understood at any point in its lifecycle. ADLC uses this audit when a request has systemic risk: multi-service behavior, non-engineer workflow creation, high AI-generated code volume, ownership gaps, or production data paths that cannot be explained.

This is not a bug hunt. It is a comprehension risk assessment.

Standalone Interview Mode

When the user asks for a dark-code audit directly, gather context one group at a time. Wait for an answer before moving to the next group.

Group 1 - System Architecture

Ask:

"Describe your system architecture. You can paste a repo structure, a list of services/modules, or describe it in plain language. I need to understand: what are the major components, how do they communicate, and where does data flow between them?"

Group 2 - AI Tool Usage

Ask:

"How does your team use AI coding tools? Specifically:

• Which tools?
• What percentage of new code is AI-generated?
• Are there mandates or targets for AI tool usage?
• Do AI agents have autonomous access such as CI/CD, production, data pipelines, or runtime tool selection?"

Group 3 - Team And Ownership

Ask:

"How is ownership structured?

• How many engineers, and what's the seniority distribution?
• Has headcount changed significantly in the past 12 months?
• Is there a clear mapping of team to service?
• Are there services or workflows that no specific team owns?"

Group 4 - Development And Deployment

Ask:

"Walk me through how code gets to production:

• What does review look like for AI-generated code vs. human-written code?
• What automated checks exist?
• Can non-engineers connect tools, create workflows, or wire agents to production data?
• Have there been incidents or near-misses in the past 6 months where the root cause was hard to trace?"

ADLC Embedded Mode

When this skill runs inside ADLC, do not stop the pipeline for a full interview unless a required fact is missing and the risk is elevated. Use these inputs first:

• PRD
• repo map
• graph research evidence
• Build Brief change surface
• owner/on-call data from docs if available
• prior incident/runbook references if provided

If team or AI-usage data is missing, mark it as insufficient data to assess. Do not invent it.

Analysis Dimensions

Structural Dark Code

Look for emergent behavior nobody designed:

• agent-assembled runtime paths
• cross-service data flows without explicit schemas
• non-engineer workflows touching production data
• tool chains where behavior emerges from agent choices
• services that interact in ways no team explicitly wired together

Velocity Dark Code

Look for authored code nobody understood:

• high AI-generation ratio without proportional review depth
• reduced senior engineering capacity relative to code volume
• AI-generated code that passes checks but was never held in anyone's head
• fast-shipping teams with no spec or design-doc requirement
• services modified frequently by AI with no comprehension artifacts

Compounding Factors

• ownership gaps
• lost institutional knowledge
• observability mistaken for comprehension
• regulatory exposure from unexplainable data processing

Output

Produce a Dark Code Risk Assessment:

Store the canonical assessment in ADLC process artifact storage with bin/adlc process-artifact-path --target-repo <target-repo> --task <task-id> --artifact-type audit --filename dark-code.json --json. Do not add the assessment to the target-repo diff unless it is intentionally converted into user-facing product documentation.

1. Executive Summary - 3-4 sentences with overall risk level: Critical, High, Moderate, or Low.
2. Dark Code Hotspot Map - table with Component/Area, Dark Code Type, Severity, Owner, Key Risk Description.
3. Highest-Risk Scenarios - top 3 concrete failure scenarios, diagnosis difficulty, blast radius, and whether the behavior could be explained to a regulator or customer.
4. Ownership Gaps - unowned behaviors, workflows, or data flows, who likely created them if known, and why they fell through the ownership model.
5. Comprehension Debt Scorecard - spec coverage, context coverage, review depth, and explainability.
6. Prioritized Action Plan - ordered actions with hotspot addressed, expected effort, and one-time vs ongoing practice.

Guardrails

• Only assess based on provided or directly verified information.
• If data is limited, say insufficient data to assess.
• Distinguish confirmed facts from risks that need investigation.
• Be direct. Critical means critical.
• Do not recommend "add more monitoring" or "add a supervisory layer" as a primary fix. Recommend comprehension infrastructure: specs, context layers, comprehension gates, ownership assignments, and explicit contracts.