archetype-review-base
About
This is a foundational review framework that all domain-specific reviewers must implement to ensure consistent structure, severity ratings, and verdict formatting. It defines the boundary between domain-specific heuristics and generic checks, eliminating duplication across 18 different reviewer prompts. Use this skill whenever invoking any listed domain reviewer, but not for general cross-domain security reviews.
Quick Install
Claude Code
Recommendednpx skills add avelikiy/great_cto -a claude-code/plugin add https://github.com/avelikiy/great_ctogit clone https://github.com/avelikiy/great_cto.git ~/.claude/skills/archetype-review-baseCopy and paste this command in Claude Code to install this skill
Documentation
Archetype-review-base — shared review framework
Every domain reviewer follows this skeleton. Each reviewer's own SKILL.md adds the domain heuristics on top. This skill defines the parts that must be IDENTICAL across all reviewers.
Mandatory report sections
A domain review report is a markdown file at
docs/reviews/REVIEW-{slug}-{reviewer}.md. It MUST contain these
sections in this exact order:
# REVIEW-{slug} — {reviewer name}
Reviewed: {commit-sha or file paths or ARCH doc reference}
Standard: {regulation / framework you applied — list specific clauses}
Date: {ISO timestamp}
## Scope
2-3 sentences. What did you look at? What's intentionally out of scope?
## Findings
For each finding, use this exact format:
- **[Critical|High|Medium|Low]** {one-sentence finding title}
- Location: {file:line or component name}
- Rationale: {why this matters IN THIS DOMAIN — cite a regulation or
domain-specific best practice. Generic "could be a problem" is
rejected.}
- Remediation: {specific fix — code change, config change, or
architectural change. NOT "consider adding X" — write the exact change.}
- References: {URL or document section}
Order findings: Critical → High → Medium → Low.
If no findings at a tier, write: "_None at {tier} severity._"
## Verdict
VERDICT: {APPROVED|BLOCKED} reason="{specific reason}"
Severity scale (DOMAIN-anchored)
Severity is graded against THIS DOMAIN's regulatory or correctness baseline, not generic STRIDE severity. Examples:
- A PCI reviewer rating an unencrypted PAN at REST = Critical (PCI scope violation; immediate regulatory exposure)
- An oracle reviewer rating a Chainlink staleness < 1h = High (likely OK now, MEV vulnerable in stress)
- A gov reviewer rating Section 508 a11y gaps = High (federal contract risk; not Critical because not an immediate breach)
Cite the standard in Rationale. If you can't, the finding is probably generic and should be reduced one severity tier (the security-officer agent handles generic concerns).
Verdict rules
VERDICT: APPROVEDis allowed only when ALL Critical and ALL High findings have remediation in the bd backlog. (Usebd ready --label {your-archetype}to check.)VERDICT: BLOCKEDis required when even one Critical or High has no remediation, OR when discovery surfaced an unknown that you couldn't resolve.- Medium and Low findings do NOT block. Note them; pipeline continues.
Domain heuristic vs generic check
You are the SPECIALIST. Your job is the domain-specific stuff that generic STRIDE / OWASP misses. Decision rule:
| The check is about… | Belongs to |
|---|---|
| Card data, PCI scope, idempotency in payments | pci-reviewer |
| Oracle staleness, MEV, contract upgradeability | oracle-reviewer |
| PHI flows, BAA chain, FHIR/HL7 | healthcare-reviewer |
| Generic XSS, SQLi, weak hashing, secrets in source | security-officer (NOT you) |
| Generic "needs error handling" | senior-dev / code-reviewer (NOT you) |
If a finding is generic, mention it briefly but DON'T inflate severity. Defer to the appropriate generic reviewer.
Apply skeptical-triage
Before emitting VERDICT: BLOCKED, apply the skeptical-triage skill
(3 rounds of self-challenge). False-positive BLOCKED at gate:plan wastes
CTO time. Only block when 3/3 rounds confirm.
Verdict log line
After writing your report, append ONE line to your verdict log:
{ISO-ts} {APPROVED|BLOCKED} feature={slug} review=docs/reviews/REVIEW-{slug}-{reviewer}.md criticals={N} highs={M} mediums={K} cost=${USD}
The board's readVerdicts() parser anchors on the leading timestamp.
Format MUST be space-separated; pipe-separated form parses as
verdict='|' and breaks the pipeline status display.
Prose rules — apply skill prose-style
- No hedge words ("generally", "somewhat", "maybe")
- Lead with the conclusion
- Concrete evidence (file:line) over adjectives
- No filler openings ("In this review, we will...")
- Verdict line on the LAST line of the report
When to escalate vs review
Escalate to security-officer (not just BLOCK) when:
- The finding crosses your domain boundary (e.g. PCI reviewer hits a generic SQLi — that's security-officer's job)
- A regulatory question is ambiguous (e.g. "is this BA or sub-processor under HIPAA?")
- The user has provided conflicting requirements (BLOCKED on contradictions, not on your domain expertise)
Escalation: create a bd task with label security-officer and
blocks your review verdict.
Self-test before sign-off
Before writing your verdict line, grep your draft for:
\b(generally|somewhat|fairly|mostly|possibly|perhaps|maybe)\b— rewrite- Any finding without a Location line — fix
- Any finding without Remediation as a SPECIFIC change — fix
- Any Critical/High without remediation-in-bd — flip to BLOCKED
If any check fires in a non-quoted block, fix before signing off.
GitHub Repository
Related Skills
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
cost-optimization
OtherThis Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
quantizing-models-bitsandbytes
OtherThis skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
dispatching-parallel-agents
OtherThis Claude Skill dispatches multiple agents to investigate and fix 3+ independent problems concurrently. It is designed for scenarios involving unrelated failures that can be resolved without shared state or dependencies. The core capability is parallel problem-solving, assigning one agent per independent problem domain to maximize efficiency.
