gstack/review/checklist.md

Pre-Landing Review Checklist

Instructions

Review the git diff origin/main output for the issues listed below. Be specific — cite file:line and suggest fixes. Skip anything that's fine. Only flag real problems.

Two-pass review:

• Pass 1 (CRITICAL): Run SQL & Data Safety and LLM Output Trust Boundary first. Highest severity.
• Pass 2 (INFORMATIONAL): Run all remaining categories. Lower severity but still actioned.

All findings get action via Fix-First Review: obvious mechanical fixes are applied automatically, genuinely ambiguous issues are batched into a single user question.

Output format:

Pre-Landing Review: N issues (X critical, Y informational)

**AUTO-FIXED:**
- [file:line] Problem → fix applied

**NEEDS INPUT:**
- [file:line] Problem description
  Recommended fix: suggested fix

If no issues found: Pre-Landing Review: No issues found.

Be terse. For each issue: one line describing the problem, one line with the fix. No preamble, no summaries, no "looks good overall."

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Review Categories

Pass 1 — CRITICAL

SQL & Data Safety

• String interpolation in SQL (even if values are .to_i/.to_f — use sanitize_sql_array or Arel)
• TOCTOU races: check-then-set patterns that should be atomic WHERE + update_all
• update_column/update_columns bypassing validations on fields that have or should have constraints
• N+1 queries: .includes() missing for associations used in loops/views (especially avatar, attachments)

Race Conditions & Concurrency

• Read-check-write without uniqueness constraint or rescue RecordNotUnique; retry (e.g., where(hash:).first then save! without handling concurrent insert)
• find_or_create_by on columns without unique DB index — concurrent calls can create duplicates
• Status transitions that don't use atomic WHERE old_status = ? UPDATE SET new_status — concurrent updates can skip or double-apply transitions
• html_safe on user-controlled data (XSS) — check any .html_safe, raw(), or string interpolation into html_safe output

LLM Output Trust Boundary

• LLM-generated values (emails, URLs, names) written to DB or passed to mailers without format validation. Add lightweight guards (EMAIL_REGEXP, URI.parse, .strip) before persisting.
• Structured tool output (arrays, hashes) accepted without type/shape checks before database writes.

Enum & Value Completeness

When the diff introduces a new enum value, status string, tier name, or type constant:

• Trace it through every consumer. Read (don't just grep — READ) each file that switches on, filters by, or displays that value. If any consumer doesn't handle the new value, flag it. Common miss: adding a value to the frontend dropdown but the backend model/compute method doesn't persist it.
• Check allowlists/filter arrays. Search for arrays or %w[] lists containing sibling values (e.g., if adding "revise" to tiers, find every %w[quick lfg mega] and verify "revise" is included where needed).
• Check case/if-elsif chains. If existing code branches on the enum, does the new value fall through to a wrong default? To do this: use Grep to find all references to the sibling values (e.g., grep for "lfg" or "mega" to find all tier consumers). Read each match. This step requires reading code OUTSIDE the diff.

Pass 2 — INFORMATIONAL

Conditional Side Effects

• Code paths that branch on a condition but forget to apply a side effect on one branch. Example: item promoted to verified but URL only attached when a secondary condition is true — the other branch promotes without the URL, creating an inconsistent record.
• Log messages that claim an action happened but the action was conditionally skipped. The log should reflect what actually occurred.

Magic Numbers & String Coupling

• Bare numeric literals used in multiple files — should be named constants documented together
• Error message strings used as query filters elsewhere (grep for the string — is anything matching on it?)

Dead Code & Consistency

• Variables assigned but never read
• Version mismatch between PR title and VERSION/CHANGELOG files
• CHANGELOG entries that describe changes inaccurately (e.g., "changed from X to Y" when X never existed)
• Comments/docstrings that describe old behavior after the code changed

LLM Prompt Issues

• 0-indexed lists in prompts (LLMs reliably return 1-indexed)
• Prompt text listing available tools/capabilities that don't match what's actually wired up in the tool_classes/tools array
• Word/token limits stated in multiple places that could drift

Test Gaps

• Negative-path tests that assert type/status but not the side effects (URL attached? field populated? callback fired?)
• Assertions on string content without checking format (e.g., asserting title present but not URL format)
• .expects(:something).never missing when a code path should explicitly NOT call an external service
• Security enforcement features (blocking, rate limiting, auth) without integration tests verifying the enforcement path works end-to-end

Completeness Gaps

• Shortcut implementations where the complete version would cost <30 minutes CC time (e.g., partial enum handling, incomplete error paths, missing edge cases that are straightforward to add)
• Options presented with only human-team effort estimates — should show both human and CC+gstack time
• Test coverage gaps where adding the missing tests is a "lake" not an "ocean" (e.g., missing negative-path tests, missing edge case tests that mirror happy-path structure)
• Features implemented at 80-90% when 100% is achievable with modest additional code

Crypto & Entropy

• Truncation of data instead of hashing (last N chars instead of SHA-256) — less entropy, easier collisions
• rand() / Random.rand for security-sensitive values — use SecureRandom instead
• Non-constant-time comparisons (==) on secrets or tokens — vulnerable to timing attacks

Time Window Safety

• Date-key lookups that assume "today" covers 24h — report at 8am PT only sees midnight→8am under today's key
• Mismatched time windows between related features — one uses hourly buckets, another uses daily keys for the same data

Type Coercion at Boundaries

• Values crossing Ruby→JSON→JS boundaries where type could change (numeric vs string) — hash/digest inputs must normalize types
• Hash/digest inputs that don't call .to_s or equivalent before serialization — { cores: 8 } vs { cores: "8" } produce different hashes

View/Frontend

• Inline <style> blocks in partials (re-parsed every render)
• O(n*m) lookups in views (Array#find in a loop instead of index_by hash)
• Ruby-side .select{} filtering on DB results that could be a WHERE clause (unless intentionally avoiding leading-wildcard LIKE)

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Severity Classification

CRITICAL (highest severity):      INFORMATIONAL (lower severity):
├─ SQL & Data Safety              ├─ Conditional Side Effects
├─ Race Conditions & Concurrency  ├─ Magic Numbers & String Coupling
├─ LLM Output Trust Boundary      ├─ Dead Code & Consistency
└─ Enum & Value Completeness      ├─ LLM Prompt Issues
                                   ├─ Test Gaps
                                   ├─ Completeness Gaps
                                   ├─ Crypto & Entropy
                                   ├─ Time Window Safety
                                   ├─ Type Coercion at Boundaries
                                   └─ View/Frontend

All findings are actioned via Fix-First Review. Severity determines
presentation order and classification of AUTO-FIX vs ASK — critical
findings lean toward ASK (they're riskier), informational findings
lean toward AUTO-FIX (they're more mechanical).

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Fix-First Heuristic

This heuristic is referenced by both /review and /ship. It determines whether the agent auto-fixes a finding or asks the user.

AUTO-FIX (agent fixes without asking):     ASK (needs human judgment):
├─ Dead code / unused variables            ├─ Security (auth, XSS, injection)
├─ N+1 queries (missing .includes())      ├─ Race conditions
├─ Stale comments contradicting code       ├─ Design decisions
├─ Magic numbers → named constants         ├─ Large fixes (>20 lines)
├─ Missing LLM output validation           ├─ Enum completeness
├─ Version/path mismatches                 ├─ Removing functionality
├─ Variables assigned but never read       └─ Anything changing user-visible
└─ Inline styles, O(n*m) view lookups        behavior

Rule of thumb: If the fix is mechanical and a senior engineer would apply it without discussion, it's AUTO-FIX. If reasonable engineers could disagree about the fix, it's ASK.

Critical findings default toward ASK (they're inherently riskier). Informational findings default toward AUTO-FIX (they're more mechanical).

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Suppressions — DO NOT flag these

• "X is redundant with Y" when the redundancy is harmless and aids readability (e.g., present? redundant with length > 20)
• "Add a comment explaining why this threshold/constant was chosen" — thresholds change during tuning, comments rot
• "This assertion could be tighter" when the assertion already covers the behavior
• Suggesting consistency-only changes (wrapping a value in a conditional to match how another constant is guarded)
• "Regex doesn't handle edge case X" when the input is constrained and X never occurs in practice
• "Test exercises multiple guards simultaneously" — that's fine, tests don't need to isolate every guard
• Eval threshold changes (max_actionable, min scores) — these are tuned empirically and change constantly
• Harmless no-ops (e.g., .reject on an element that's never in the array)
• ANYTHING already addressed in the diff you're reviewing — read the FULL diff before commenting