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This version passed the hardening gate. The skill content was retained without changes. Classified findings before hardening: 1. Classified findings closed: 0. Classified findings remaining: 1. Unclassified scanner findings remaining: 0. Review findings closed: 0. Review findings remaining: 9.
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Expand | File | Group | Subtype | Severity | Status |
|---|---|---|---|---|---|
references/key-naming.md | Prompt Injection | Third-party content as instructions | Low | Open |
1 finding
Foundational guidance for modeling data in Redis. Covers data-type selection and key-name conventions — the two decisions that most directly drive memory, performance, and maintainability.
Pick the type that matches the access pattern, not just the shape of the data.
| Use case | Recommended type | Why |
|---|---|---|
Simple values, counters | String | Atomic |
Object with independently updated fields | Hash | Per-field reads/writes, no whole-object rewrite |
Queue, recent-N items | List | O(1) push/pop at ends |
Unique items, membership checks | Set | O(1) |
Rankings, score-based ranges | Sorted Set | Score-ordered; |
Nested / hierarchical data | JSON | Path-level updates, nested arrays, RQE indexing |
Event log, fan-out messaging | Stream | Persistent, consumer groups |
Vector similarity | Vector Set | Native vector storage with HNSW |
Common anti-pattern: stuffing a flat object into a serialized string. Updating one field means fetch + parse + mutate + rewrite. Use a Hash instead.
See references/choose-data-structure.md for full rationale and Python/Java examples.
Use colon-separated segments with a stable hierarchy:
Rules of thumb:
User_1001_Profile is bad).tenant:42:user:7:cart) so scans and ACLs can target a tenant cleanly.See references/key-naming.md for cleanup examples and edge cases.
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SKILL.md
evals/core/baselines/README.md
evals/core/baselines/aggregate-benchmark.json
evals/core/baselines/aggregate-benchmark.md
evals/core/baselines/baseline.json
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evals/core/evals.json
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references/choose-data-structure.md
references/key-naming.md