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 skills/andyzengmath/quantum-loop/ql-deep-reviewnpx skills add andyzengmath/quantum-loop --skill ql-deep-reviewgit 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 | $0.00079 | $0.02668 |
| Opus 5 | $0.00039 | $0.01334 |
| Sonnet 5 | $0.00016 | $0.00534 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade A, and why
ql-deep-review 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ql-deep-review — whole-feature post-implementation review
Purpose
quantum-loop's built-in two-stage review gate (ql-review: spec-compliance → code-quality) operates on ONE story at a time inside ONE worktree. It does not detect:
- Cross-story divergence (e.g., story A uses
'google'as a secret key while story B uses'google-api-key'for the same constant). - Post-merge regressions (test that was green in isolation breaks after integration).
- Drift from original user intent (paraphrase chain from intent → design → PRD → plan → code).
- Low-signal comments that look like findings but lack evidence (CRA actionability is 0.9-19.2% per Chowdhury 2604.03196; human baseline is ~60%).
ql-deep-review closes these gaps with a whole-feature review that runs AFTER all stories in a wave / feature pass the per-story gate.
When to use
- After
ql-executeemitsCOMPLETEfor a wave and before merging the feature branch to master. - After cherry-picking or merging a foreign branch whose conflict-resolution changed semantics.
- Manually, when suspicion of cross-story drift is high (e.g., follow-on work after a long autonomous run).
What it does NOT do
- Does not replace the per-story two-stage gate. Run
ql-reviewper story, then this. - Does not auto-fix findings. Produces a structured report; user or orchestrator drives action.
- Does not block merge autonomously. Emits a verdict + confidence; user decides.
Risk scoring (0-100)
Risk factors and weights (inspired by soliton's risk-adaptive dispatch):
| Factor | Weight | Measurement |
|---|---|---|
| Blast radius | 25 | count of files touched in wave × (max transitive callers of any touched symbol ÷ 100) |
| Change complexity | 15 | difftastic or cloc diff line count; tree-sitter function edit count |
| Sensitive paths | 20 | glob match: auth/, payment/, *.env*, *secret*, *password*, *token* |
| File size / scope | 10 | total LOC touched / number of files |
| AI-authored signal | 10 | git commit trailer Co-Authored-By: Claude, uniform-style heuristic |
| Test coverage gap | 10 | production files touched without corresponding test edits |
| Intent-drift signal | 10 | ql-intent-check CRITICAL findings count (optional input) |
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 · 197 lines · 79 tokens per session scan A 93e986af54c1
ql-deep-review is a skill published in the GitHub repository andyzengmath/quantum-loop (24 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 2,668 once invoked, about $0.0004 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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