predict-issues

A codebase review that looks for patterns likely to cause future problems, such as slow algorithms, security gaps, growing complexity, and fragile assumptions.

In plain words
What is it for?
Use it to inspect critical code, find technical debt, assess scalability, and prioritize fixes before a project grows.
Why use it?
It helps identify risks before they become bugs, outages, or expensive maintenance work. The review considers how likely each problem is, how serious it could be, and how difficult it may be to fix.

Command

Install

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.

agentmods
npx agentmods add commands/brennercruvinel/ccplugins/predict-issues
Clone the repo
git clone --depth 1 https://github.com/brennercruvinel/CCPlugins
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 617 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00617
Opus 5 $0.00000 $0.00309
Sonnet 5 $0.00000 $0.00123
Haiku 4.5 $0.00000 $0.00062

Measured 2d ago against content hash 6c00718f4541, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

predict-issues 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.

commands/predict-issues.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Predictive Code Analysis

I'll analyze your codebase to predict potential problems before they impact your project.

Strategic Thinking Process

  1. Pattern Recognition

    • Which code patterns commonly lead to problems?
    • Are there growing complexity hotspots?
    • Do I see anti-patterns that will cause issues at scale?
    • Are there ticking time bombs (hardcoded values, assumptions)?
  2. Risk Assessment Framework

    • Likelihood: How probable is this issue to occur?
    • Impact: How severe would the consequences be?
    • Timeline: When might this become a problem?
    • Effort: How hard would it be to fix now vs later?
  3. Common Problem Categories

    • Performance: O(n²) algorithms, memory leaks, inefficient queries
    • Maintainability: High complexity, poor naming, tight coupling
    • Security: Input validation gaps, exposed secrets, weak auth
    • Scalability: Hardcoded limits, single points of failure
  4. Prediction Strategy

    • Start with highest risk areas (critical path code)
    • Look for patterns that break at 10x, 100x scale
    • Check for technical debt accumulation
    • Identify brittleness in integration points

Based on this analysis framework, I'll use native tools for comprehensive analysis:

  • Grep tool to search for problematic patterns
  • Glob tool to analyze file structures and growth
  • Read tool to examine complex functions and hotspots

I'll examine:

  • Code complexity trends and potential hotspots
  • Performance bottleneck patterns forming
  • Maintenance difficulty indicators
  • Architecture stress points and scaling issues
  • Error handling gaps

For each prediction, I'll:

  • Show specific code locations with file references
  • Explain why it's likely to cause future issues
  • Estimate potential timeline and impact
  • Suggest preventive measures with priority levels

When I find multiple issues, I'll create a todo list for systematic review and prioritization.

Read the full file on GitHub · 79 lines

Changes

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.

  1. 2d ago First seen · 79 lines · 0 tokens per session scan A 6c00718f4541

Subscribe to this mod's changes

predict-issues is a command published in the GitHub repository brennercruvinel/CCPlugins (2,780 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 617 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.