
Gjalla Spec Review
- 220 installs
- 2 repo stars
- Updated June 15, 2026
- gjalla/engineering
Reviews a plan or spec to surface surprises, gaps, or mistakes before implementation.
About
Performs a full review of a plan or spec to catch gaps, surprises, and mistakes. A developer uses it to harden a spec before starting implementation.
- Finds gaps and surprises in a spec
- Hardens the plan before implementation
Gjalla Spec Review by the numbers
- 220 all-time installs (skills.sh)
- Ranked #1,013 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Data as of Jul 24, 2026 (Skillselion catalog sync)
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| Installs | 220 |
|---|---|
| repo stars | ★ 2 |
| Last updated | June 15, 2026 |
| Repository | gjalla/engineering ↗ |
What it does
Reviews a plan or spec to surface surprises, gaps, or mistakes before implementation.
Files
Comprehensive spec-review
The best, most elegant and effective software systems are ones that are well-informed, well-planned, and verifiable. The final output is a plan/spec that can act as a reference doc, covering the problem/motivation, technical approach, deltas (what properties of the system will change once implemented), and verification criteria.
Your task is to review this spec from multiple expert perspectives to ensure that this spec meets our expectations and will result in solid implementations once it's in the hands of the engineers:
Architect Review
- Does the design fit the existing system and respect layer boundaries?
- Are new components placed in the correct layer and ownership hierarchy?
- Do interactions follow established patterns (sync/async/event)?
- Is it compatible with the broader system (i.e. cross repository integrations, future goals, etc.?)
- Is it overengineered or introducing complication that will be difficult to understand/maintain?
- gjalla holds the master architecture spec, referencing it is key to ensuring that we're building an elegant and effective system
Security Review
- Are all new endpoints authenticated and authorized?
- Does the data model handle sensitive data correctly (PII, secrets)?
- Are inputs validated at the surface-area boundary?
- Are the data flows changing in a way the user should know about, like different types of data flowing to new consumers, etc.?
Quality Review
- Is the testing strategy sufficient for the behavioral requirements?
- The goal for AI-generated code is typically 100% test coverage, are positive, negative, and edge cases covered? Are appropraite and effective integration tests scope based on the end-goals?
- Do acceptance criteria map to testable assertions?
User Advocate Review
- Does the feature solve the stated problem?
- Are failure modes graceful from the user's perspective?
- Is the feature discoverable and documented?
Governance Review
- Does the spec comply with all active project rules and conventions? (gjalla's master spec also defines rules and processes, check those)
- Are any rule exceptions or overrides needed?
- Does this change warrant an architecture decision record (ADR)? (not all do, mostly just things that introduce significant, new tradeoffs and need a historical record)
Output
For each lens, report: what passes, what's at risk, and concrete changes required before implementation. Separate blocking concerns from recommendations.