By galando
Enforce quality gates on AI-generated code with blast radius analysis, confidence scoring, and intent-driven workflows — plan, implement, review, and validate changes via TDD pipelines and adversarial design checks.
Plan feature with impact analysis and blast radius
System design exploration for complex features
Execute plan with TDD and quality gates
Technical code review with confidence scoring, review memory, and intent validation
Run stack-aware validation pipeline
Temper core: stack detection, quality gates, blast radius, adaptive learning
Hierarchical context loading for AI coding agents — load what you need, defer what you don't
Version-aware, source-driven development — fetch official docs before writing framework code
Socratic challenge mode — stress-test plans and designs with adversarial questions, one at a time
Comprehension companion — incrementally teach the user every change, confirm mastery before advancing, and quiz to close gaps. Keeps the human engaged across all phases.
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Your AI writes fast. Temper makes it last.
Quality gates, blast radius analysis, and intent-driven development for AI-generated code
Quick Start:
/plugin marketplace add galando/temperthen/temper "add password reset"— one command for the full pipeline.
AI writes code fast. But "fast" without "right" creates bugs, technical debt, and features that miss the point. AI-generated code has structural failure patterns:
| Pattern | What Goes Wrong |
|---|---|
| Missing behaviors | Happy path works, edge cases never implemented |
| Wrong problem solved | Feature works perfectly, but nobody asked for it |
| Over-engineering | Factories and strategies for something used once |
| Hallucinated APIs | Methods called that don't exist |
| Missing wiring | Code correct, integration missing |
Most AI tools check if code compiles. Temper checks if it solves the right problem, handles edge cases, and is safe to ship.
The rate-limiting bug that vanilla AI always misses:
Scenario: Rate limiting on reset requests
Given a user has requested 3 resets in 10 minutes
When they request another reset
Then the request is rejected with 429
AI built password reset. All tests pass. But Temper's scenario coverage gate caught the gap: no test for rate limiting. Build wrote the test. Test failed. Build implemented rate limiting. Test passed. Without the coverage gate, rate limiting would never have been implemented.
More: Evidence Gallery
Three methodologies, one contract file (intent.md):
intent.md
|
+-- Intent (IDD) WHY are we building this?
| Problem, success criteria, constraints
|
+-- Scenarios (BDD) WHAT should it do?
| Gherkin Given/When/Then, derived BEFORE architecture
|
+-- /temper:build (TDD) HOW do we build it?
Tests from scenarios, RED → GREEN → REFACTOR
Key insight: Scenarios are derived before architecture. The file plan follows from what the system must do, not the other way around. This prevents over-engineering structurally.
Full methodology: docs/methodology.md
After you approve the plan, /temper can run the remaining stages
(design → build → review → check → eval) unattended and leave a report.
It never pushes or merges, never re-plans on its own, and parks before
commit and on anything needing a human. Turn it off and Temper behaves
exactly as before.
First run: pre-allow your build/test commands in settings.json, or the run
parks on the first unpermitted command. No config yet? /temper:init seeds
one. Details: docs/proposals/autonomous-mode.md
Temper cuts a run's token cost with three optimizations — all ON by default, all revert to v5.8.0 when their flag is off:
| Optimization | Default | Quality tradeoff |
|---|---|---|
| Cache static methodology reads | on | None — same reads, ~90% off re-read cost |
| Adaptive depth — size the pipeline to the change | on (floor: simple) | Only one with a tradeoff — see below |
| Incremental loops — inline auto-fix, no subprocess | on (inline-threshold: 3) | None for auto-fixable findings |
The one decision you make: adaptive-depth runs a lighter pipeline on small changes. It's safe when the change is genuinely isolated — but if it touches a shared interface, auth, money, or has an unclear blast radius, click "Escalate to full pipeline" at the plan gate (one click, that change only — no config editing). For a whole high-stakes codebase, set floor: medium in .claude/temper.config once and never think about it again.
Full explanation (mechanism, tiers, decision rule, when quality drops): docs/token-efficiency.md
npx claudepluginhub galando/temper --plugin temperPIV + Spec-Kit: PIV methodology with structured specs and strict TDD
Mindful AI coding framework — discipline over cleverness. Skill + 21 slash commands + 8 specialist agents + 5 runtime hooks + 15 default checklists + Master Orchestrator + Gravity hub. Works on any model tier (Opus/Sonnet/Haiku). Integrates Claude Design for visual work.
Auto-loop execution workflow with quality gates for Claude Code. Automatically decomposes tasks, implements code, runs tests, and iterates through quality gates until completion.
AI-powered development workflow automation - Phase-based planning, implementation orchestration, preflight code quality checks with security scanning, ship-it workflow, and development principles generator for CLAUDE.md
Multi-agent orchestration for code that matters.
Verification-first engineering toolkit for Claude Code. 15 skills across a 5-phase spine (Investigate → Design → Implement → Verify → Ship), 8 specialist agents, an interactive setup wizard. Every skill has rationalizations + evidence requirements. Built for senior ICs and tech leads.
The only Claude Code plugin that verifies AI-generated code against its own design specs.