.agents/skills/code-review/SKILL.md
Three-stage code review protocol covering spec compliance, code quality, and domain integrity. Use this skill whenever the user asks to review code, prepare or check a PR, assess implementation quality, verify code against a spec or acceptance criteria, or audit for security and domain modeling issues. Triggers on: "review this code", "review my PR", "check implementation against spec", "code quality audit", "does this match the requirements", "review for security issues", "check for primitive obsession", "monetary precision review", "review test coverage gaps". Also activates when the user wants structured PASS/FAIL verdicts per requirement, severity-rated findings, or a gated review that blocks on critical issues. NOT for: style/formatting linting, debugging runtime errors, writing new code, or automated CI checks.
npx skillsauth add G858-debug/No-Safe-Word code-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Value: Feedback and communication -- structured review catches defects that the author cannot see, and separating review into stages prevents thoroughness in one area from crowding out another.
Teaches a systematic three-stage code review that evaluates spec compliance, code quality, and domain integrity as separate passes. Prevents combined reviews from letting issues slip through by ensuring each dimension gets focused attention.
Review code in three sequential stages. Do not combine them. Each stage has a single focus. A failure in an earlier stage blocks later stages -- there is no point reviewing code quality on code that does not meet the spec.
Stage 1: Spec Compliance. Does the code do what was asked? Not more, not less.
For each acceptance criterion or requirement:
Mark each criterion: PASS, FAIL (missing/incomplete/divergent), or CONCERN (implemented but potentially incorrect). Flag anything built beyond requirements as OVER-BUILT.
If any criterion is FAIL, stop. Return to implementation before continuing.
Architecture Compliance Check (run after the per-criterion loop, before moving to Stage 2):
docs/ARCHITECTURE.md exists: verify this change complies with all
documented constraints and patterns (Components, Patterns, Constraints
sections). Non-compliance is a FAIL — same severity as a missing acceptance
criterion.docs/ARCHITECTURE.md does not exist: flag as a Stage 2 CONCERN:
"No architecture document found; architectural compliance cannot be verified."Include in Stage 1 output: Architecture Compliance: PASS / FAIL / N/A (no ARCHITECTURE.md)
For tasks that implement a vertical slice (adding user-observable behavior), perform the following checks in order:
Entry-point wiring check (diff-based): Examine whether the changeset includes modifications to the application's entry point or its wiring/routing layer. If the slice claims to add new user-observable behavior but the diff does not touch any wiring or entry-point code, the review fails unless the author explicitly documents why existing wiring already routes to the new behavior.
End-to-end traceability: Verify that a path can be traced from the application's external entry point, through any infrastructure or integration layer, to the new domain logic, and back to observable output. If any segment of this path is missing from the changeset and not already present in the codebase, flag the gap.
Boundary-level test coverage: Confirm that at least one test exercises the new behavior through the application's external boundary (e.g., an HTTP request, a CLI invocation, a message on a queue) rather than calling internal functions directly. Where the application architecture makes automated boundary tests feasible, their absence is a review concern.
Test-level smell check: If every test in the changeset is a unit test of isolated internal functions with no integration or acceptance-level test, flag this as a concern. The slice may be implementing domain logic without proving it is reachable through the running application.
Stage 2: Code Quality. Is the code clear, maintainable, and well-tested?
Review each changed file for:
domain-modeling skill for primitive obsession detection.Categorize findings by severity:
If any CRITICAL issue exists, stop. Return to implementation.
Stage 3: Domain Integrity. Final gate -- does the code respect domain boundaries?
Check for:
domain-modeling bool-as-state check.)Flag issues but do not block on suggestions, EXCEPT convention violations -- those are blocking per the Convention Over Precedent rule.
Produce a structured summary after all three stages:
REVIEW SUMMARY
Stage 1 (Spec Compliance): PASS/FAIL
Stage 2 (Code Quality): PASS/FAIL/PASS with suggestions
Stage 3 (Domain Integrity): PASS/FAIL/PASS with flags
Overall: APPROVED / CHANGES REQUIRED
If CHANGES REQUIRED:
1. [specific required change]
2. [specific required change]
After completing all three stages, produce a REVIEW_RESULT evidence packet containing: per-stage verdicts {stage, verdict (PASS/FAIL), findings [{severity, description, file, line?, required_change?}]}, overall_verdict, required_changes_count, blocking_findings_count.
When pipeline-state is provided in context metadata, the code-review skill
operates in pipeline mode and stores the evidence to
.factory/audit-trail/slices/<slice-id>/review.json. When running
standalone, the evidence is informational only (not stored).
In factory mode, the full team reviews before the pipeline pushes code --
this is the quality checkpoint that replaces consensus-during-build. All
blocking review feedback must be addressed before push. See
references/mob-review.md for the factory mode review subsection.
Review findings MUST be written to .reviews/ files as the default
persistence mechanism. Messages are supplementary coordination signals
only — they do not survive context compaction.
<reviewer-name>-<task-slug>.md (e.g., kent-beck-user-login.md).reviews/ directory (add to .gitignore)This ensures review findings survive context compaction, agent restarts, and harnesses that lack inter-agent messaging.
Non-blocking items (SUGGESTION severity) that appear in 2+ consecutive
reviews of different slices MUST escalate to blocking (IMPORTANT severity).
Track recurrence by checking previous review files in .reviews/.
This prevents persistent quality issues from being perpetually deferred as "just a suggestion."
When a GWT scenario describes user-visible behavior (UI elements, displayed messages, visual changes), the changeset MUST include code that produces that visible output. An API-only implementation when the scenario describes UI interaction is a spec compliance failure — the slice is incomplete.
Written conventions override observed patterns. When a review finding conflicts with a project convention (CLAUDE.md, AGENTS.md, crate-level docs, architectural decision records) but matches existing code in the codebase, the finding is still valid. Existing code that violates a convention is tech debt, not precedent.
Rules:
Example: a project convention says "use the typestate pattern for state
machines." The new code uses struct Foo { is_active: bool } because three
existing files do the same. The review must block the new code AND note the
three existing files as tech debt.
When your review finding conflicts with the implementation approach:
You exist to catch what the author missed, not to block progress.
During Stage 1, also consider:
These are not blocking concerns but should be noted when relevant.
Hard constraints:
[H][RP]See CONSTRAINT-RESOLUTION.md in the template directory for pipeline
rework budget conflicts.
After completing a review guided by this skill, verify:
.reviews/ files (not messages only)If any criterion is not met, revisit the relevant stage before finalizing.
This skill works standalone. For enhanced workflows, it integrates with:
Missing a dependency? Install with:
npx skills add jwilger/agent-skills --skill domain-modeling
development
# SDXL Character LoRA Training — Pipeline Reference ## Overview Character LoRAs are trained using Kohya sd-scripts (`sdxl_train_network.py`) on RunPod GPU pods. Training runs as a fire-and-forget batch job — the orchestrator creates the pod, the pod trains, uploads the result, and POSTs a webhook on completion. **Base model for training:** SDXL 1.0 base (NOT Juggernaut Ragnarok). Training against the base model produces portable LoRAs that work across all SDXL fine-tunes (Juggernaut, RealVisX
testing
# Juggernaut XL Ragnarok — Pipeline Reference ## Overview Juggernaut XL Ragnarok is a photorealistic SDXL checkpoint. It is the most downloaded SDXL model (520K+ downloads) and the final SDXL release from KandooAI / RunDiffusion. **Key characteristics:** - Photorealistic output with cinematic quality - NSFW capability baked into training (trained with Booru tags on an NSFW dataset, merged with a Lustify-based NSFW pass for anatomical stability) - Supports BOTH natural language prompts AND Boo
tools
# Image Editing Workflows — Pipeline Reference ## Overview After initial image generation, the user has access to several post-generation editing tools. These are ComfyUI workflow variants that modify an existing generated image rather than generating from scratch. All editing workflows run on the same RunPod serverless infrastructure as generation, using the same Juggernaut Ragnarok checkpoint. ## Inpainting **Purpose:** Fix a specific region of a generated image without regenerating the w
testing
When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth ideas,' 'how to market,' 'marketing strategies,' 'marketing tactics,' 'ways to promote,' 'ideas to grow,' 'what else can I try,' 'I don't know how to market this,' 'brainstorm marketing,' or 'what marketing should I do.' Use this as a starting point whenever someone is stuck or looking for inspiration on how to grow. For specific channel execution, see the relevant skill (paid-ads, social-content, email-sequence, etc.).