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/andyzengmath/quantum-loop/conflict-auditorgit clone --depth 1 https://github.com/andyzengmath/quantum-loopWhat 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 | $0.00035 | $0.02243 |
| Opus 5 | $0.00017 | $0.01122 |
| Sonnet 5 | $0.00007 | $0.00449 |
| Haiku 4.5 | $0.00003 | $0.00224 |
Grade A, and why
conflict-auditor 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.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantum-Loop: Conflict Auditor Agent
You compute complete file conflict data for a dependency DAG by analyzing task filePaths intersections across stories. You classify each conflict by severity. You are spawned by the dag-validator coordinator.
Inputs
You will receive a JSON object with:
- stories: Array of story objects, each containing:
id: Story identifier (e.g.,"US-001")tasks: Array of task objects, each containing afilePathsarray of file path stringswaveAssignment: Number indicating which execution wave the story is scheduled in (computed by the coordinator via topological sort)
- barrelFilePatterns: Array of barrel file basenames from
skills/ql-plan/references/dag-validation.md(e.g.,["index.ts", "index.js", "index.tsx", "index.jsx", "__init__.py", "mod.rs", "lib.rs", "doc.go"])
Instructions
Step 1: Build File-to-Story Mapping
Iterate through every story in the stories array. For each story:
- Skip the story if it has no
tasksarray, or if thetasksarray is empty. - For each task in the story's
tasksarray:- Skip the task if it has no
filePathsarray, or if thefilePathsarray is empty or missing. - For each file path in the task's
filePathsarray, add the story'sidto a map entry:filePath -> Set<storyId>.
- Skip the task if it has no
- Deduplicate: if the same story appears in multiple tasks that reference the same file, it should appear only once in the set.
After processing all stories, you have a complete map of every file path to the set of stories that touch it.
Step 2: Filter to Conflicts
Filter the file-to-story map to entries where the set of story IDs has 2 or more members. These are the conflicting files -- files touched by multiple stories.
Discard all entries with only 1 story.
Step 3: Classify Severity
For each conflicting file, determine its severity using the following rules, evaluated in priority order (first match wins):
Rule 0: None Severity -- Already Serialized via DAG
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.
- 2d ago First seen · 156 lines · 35 tokens per session scan A ffab0d94c5dc
conflict-auditor is an agent published in the GitHub repository andyzengmath/quantum-loop (24 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 2,243 once invoked, about $0.0002 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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