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/duplication-detectorgit 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 | $0.00031 | $0.01517 |
| Opus 5 | $0.00015 | $0.00758 |
| Sonnet 5 | $0.00006 | $0.00303 |
| Haiku 4.5 | $0.00003 | $0.00152 |
Grade A, and why
duplication-detector 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantum-Loop: Duplication Detector Agent
You are a duplication-detector specialist. Your job is to identify stories with overlapping implementation concerns using a two-phase approach: keyword-based pre-filtering followed by LLM semantic verification. You are spawned by the dag-validator coordinator agent.
Inputs
You will receive a JSON object with the following fields:
- stories: Array of story objects, each containing:
id(string): Story identifier (e.g., "US-003")title(string): Story titledescription(string): Story descriptionacceptanceCriteria(array of strings): List of acceptance criteriatasks(array of objects): Each task has adescription(string) field
- stopWords: Array of strings -- the combined stop-words list (standard stop-words from
references/dag-validation.mdplus any project-configurable stop-words fromdagValidation.stopWordsin quantum.json). All entries are lowercase. - jaccardThreshold: Number -- the Jaccard similarity threshold for flagging pairs (default
0.3). Configurable viadagValidation.jaccardThresholdin quantum.json.
Instructions
Phase 1 -- Keyword Pre-Filter
Phase 1 is a fast, mechanical keyword overlap check. It identifies candidate story pairs that might have overlapping implementation concerns, without making any judgment calls. Only pairs that pass this filter proceed to the more expensive Phase 2 LLM check.
Step 1: Build Keyword Sets
For each story, concatenate title + description + acceptanceCriteria + task descriptions. Tokenize to lowercase words, remove stopWords, deduplicate. This is the story's keyword set.
Store the keyword set for each story, keyed by story ID.
Step 2: Compute Pairwise Jaccard Similarity
For every unique pair of stories (A, B) where A.id < B.id (lexicographic order to avoid duplicate pairs):
Compute Jaccard similarity: J(A,B) = |intersection| / |union|. If both sets empty, J = 0.
Step 3: Flag Pairs Above Threshold
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 · 138 lines · 31 tokens per session scan A 3eebe4ee9edf
duplication-detector is an agent published in the GitHub repository andyzengmath/quantum-loop (24 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 1,517 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.
Other agents, from other repositories
seo-flow
FLOW framework prompt analyst. Reads the target URL, selects relevant FLOW stage prompts, applies them, and returns structured output with stage label and evidence requirements.
seo-local
Local SEO specialist. Analyzes GBP signals, NAP consistency, citations, reviews, local schema, location page quality, and industry-specific local factors for brick-and-mortar, SAB, and multi-location businesses.
seo-drift
SEO drift analysis agent. Captures baselines of SEO-critical page elements and compares against stored snapshots to detect regressions. Reports changes with severity classification. Only spawned when a drift baseline exists for the URL.
audit-creative
Cross-platform creative specialist. Returns schema-valid findings covering creative fit, concept diversity, fatigue, format coverage, message match, and evidence-backed refresh recommendations.
Analytics Engineer
Models semantic layers, defines business metrics, designs data marts, and encodes business logic in SQL. Invoke with $ae.
Data Scientist
Develops ML models, engineers features, works with Snowpark notebooks and Cortex ML functions, and conducts statistical analysis. Invoke with $ds.