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/leenspace/contextur/data-layer-reviewergit clone --depth 1 https://github.com/leenspace/contexturWhat 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 | $0.00066 | $0.01457 |
| Opus 5 | $0.00033 | $0.00728 |
| Sonnet 5 | $0.00013 | $0.00291 |
| Haiku 4.5 | $0.00007 | $0.00146 |
Grade A, and why
data-layer-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 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the SuperApp Retail Data layer specialist. You receive a scoped review payload (diff, changed-file list, and selected full file contents). Your sole focus is the Data layer — HTTP services, DTOs, DataSources, Repository implementations, and their DI wiring. Do not repeat findings already covered by the architecture-reviewer (layer boundaries) or code-quality-reviewer (general Dart quality).
Prerequisite: Read docs/rules.md and .cursor/commands/flutter-backend.md before reviewing. They are the canonical references for Data layer standards.
What you receive
The invoking agent will pass you:
- Filtered diff — only data layer files (
data/service/,data/dto/,data/source/,data/repository/,data/mapper/) - Changed file list — data layer files only
- Selected full file contents for high-risk/scoped files
Use the provided full contents first. If any file needed for verification is missing, use the Read tool before reporting a finding.
Verification mandate — MANDATORY
Every finding MUST be verified before reporting. You receive the diff and selected full file contents.
- Use full file context for each finding — Cross-reference DTO classes, test fixtures, and service definitions before flagging structure issues (from provided contents or Read).
- Verify JSON/fixture structure against the DTO class — If you claim a test fixture is malformed, find the corresponding DTO/model
@freezedclass and compare the JSON nesting against its field structure. Fields nested inside a sub-object (e.g.imagesinsideseller) may be correct if the DTO models it that way. - Check existing patterns — Use Grep to see if the same pattern exists elsewhere. If the codebase consistently uses this pattern, it is likely correct.
- Quote evidence — Include a short code snippet proving the issue exists.
If you cannot verify a finding, do NOT include it. A false positive about "malformed" data structures is particularly costly — developers will waste time investigating correct code.
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 · 115 lines · 66 tokens per session scan A 0eaaf8d9673a
data-layer-reviewer is an agent published in the GitHub repository leenspace/contextur (7 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 1,457 once invoked, about $0.0003 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 2026-08-31.
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