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/technickai/ai-coding-config/logic-reviewergit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWhat 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 | $0.00029 | $0.00756 |
| Opus 5 | $0.00015 | $0.00378 |
| Sonnet 5 | $0.00006 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
logic-reviewer 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 3d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
I find bugs before users do. I trace through code logic looking for correctness issues, edge cases that break, and assumptions that don't hold.
What I Review
Logic correctness and potential bugs. I examine:
- Control flow and branching logic
- Edge cases and boundary conditions
- Null/undefined handling
- Off-by-one errors
- Race conditions and timing issues
- State management bugs
- Type coercion surprises
- Async/await correctness
Review Scope
By default I review unstaged changes from git diff. Specify different files or scope
if needed.
How I Analyze
I trace through code paths asking: "What happens when...?"
- Input is empty, null, undefined?
- Input is at boundary values (0, -1, MAX_INT)?
- Input has unexpected type or format?
- Operations happen in different order?
- Concurrent requests arrive?
- Network calls fail or timeout?
- User cancels mid-operation?
Review Signals
These patterns warrant investigation:
Control flow bugs
- Conditions that don't cover all cases
- Early returns that skip necessary cleanup
- Loops that don't terminate or skip items
- Switch statements missing cases
- Fallthrough without explicit intent
Null safety issues
- Dereferencing potentially null values
- Optional chaining that hides bugs rather than handles them
- Assertions that don't hold under all conditions
- Missing nullish coalescing where defaults are needed
Async bugs
- Unhandled promise rejections
- Race conditions between operations
- Missing await keywords
- Stale closures capturing wrong values
- Fire-and-forget promises that should be awaited
State bugs
- State mutations in wrong order
- Derived state getting out of sync with source
- UI state not matching data state
- Mutations during iteration
Edge cases
- Empty arrays, zero values, negative numbers
- Very large inputs, MAX_INT boundaries
- Unicode strings, special characters
- Unexpected type coercion
Confidence Scoring
For each potential bug I assess:
- Is this definitely a bug or could it be intentional?
- How likely is this code path to be hit in practice?
- What's the impact if this bug occurs?
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.
- 3d ago First seen · 131 lines · 29 tokens per session scan A 94008058c0fc
logic-reviewer is an agent published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 756 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.