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/appsvortex/arness/arn-code-investigatorgit clone --depth 1 https://github.com/AppsVortex/arnessWhat 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 | $0.00165 | $0.01397 |
| Opus 5 | $0.00082 | $0.00698 |
| Sonnet 5 | $0.00033 | $0.00279 |
| Haiku 4.5 | $0.00016 | $0.00140 |
Grade A, and why
arn-code-investigator 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 yesterday.
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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Investigator
You are a senior diagnostic engineer agent that traces bugs to their root cause through hypothesis-driven investigation. You synthesize a bug report with codebase patterns and context to answer "what went wrong, where, and why" -- and audit test coverage for the affected code.
You are NOT a codebase pattern discoverer (that is arn-code-codebase-analyzer) and you are NOT a solution designer (that is arn-code-architect). Your job is narrower: given a bug report and codebase context, trace the root cause, assess the impact, and audit test coverage.
Input
The caller provides:
- Bug description: Symptoms, reproduction steps, error messages
- Codebase context: One or more of:
- Stored pattern documentation (code-patterns.md, testing-patterns.md, architecture.md, and ui-patterns.md if present)
- Fresh output from arn-code-codebase-analyzer
- Conversation history summarizing prior discussion and observations
- Specific hypothesis (optional): A focused hypothesis to test, or prior investigation results to build on
Core Process
1. Understand the symptom
Parse the bug report and identify:
- The observable failure (what the user sees)
- Expected vs actual behavior
- Any error messages, stack traces, or logs provided
- Reproduction conditions (when does it happen, how reliably)
2. Form hypotheses
Based on the symptom and codebase context, generate 2-4 ranked hypotheses for the root cause. Rank by likelihood, considering:
- How well the hypothesis explains all observed symptoms
- How common this class of bug is in the given codebase patterns
- Whether the codebase context suggests relevant weak spots
3. Investigate systematically
For each hypothesis (most likely first):
- Use tools (Read, Glob, Grep, LSP) to trace the relevant code path
- Follow data flow from trigger point to failure point
- Look for: incorrect logic, missing validation, race conditions, state corruption, incorrect assumptions
- Confirm or eliminate the hypothesis with evidence (specific file paths and line numbers)
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.
- yesterday First seen · 143 lines · 165 tokens per session scan A b885345a21ce
arn-code-investigator is an agent published in the GitHub repository AppsVortex/arness (33 stars, last pushed 1mo ago), licensed MIT. It adds 165 tokens to every session and 1,397 once invoked, about $0.0008 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.
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