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Prompt optimizer

Skill asong56/skills/04-assure/prompt-optimizer

268 AI coding assistant skills, organized across 12 workflow layers. Sources include Anthropic official, FRM, SKC, LRN, SKA, and other mainstream AI coding frameworks.

Install
npx -y skills add asong56/skills --skill prompt-optimizer

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What its author says it does

Copied from the file, not written here

Analyze and rewrite a draft prompt to maximize effectiveness within the SKC skill ecosystem. Triggers: 'optimize this prompt', 'improve my prompt', 'help me write a better prompt for...', 'how should I use SKC for...', or when the user pastes a draft prompt and asks for feedback. Advisory only — outputs an optimized prompt, never executes the underlying task.

SKILL.md

14.4 KB, as published. Nobody here has run it

Prompt Optimizer

Analyze a draft prompt, critique it, match it to SKC ecosystem components, and output a complete optimized prompt the user can paste and run.

When to Use

  • User says "optimize this prompt", "improve my prompt", "rewrite this prompt"
  • User says "help me write a better prompt for..."
  • User says "what's the best way to ask Claude Code to..."
  • User says "prompt", "prompt", "prompt", ""
  • User pastes a draft prompt and asks for feedback or enhancement
  • User says "I don't know how to prompt for this"
  • User says "how should I use SKC for..."
  • User explicitly invokes /prompt-optimize

Do Not Use When

  • User wants the task done directly (just execute it)
  • User says "", "", "optimize this code", "optimize performance" — these are refactoring tasks, not prompt optimization
  • User is asking about SKC configuration (use configure-ecc instead)
  • User wants a skill inventory (use skill-stocktake instead)
  • User says "just do it" or ""

How It Works

Advisory only — do not execute the user's task.

Do NOT write code, create files, run commands, or take any implementation action. Your ONLY output is an analysis plus an optimized prompt.

If the user says "just do it", "", or "don't optimize, just execute", do not switch into implementation mode inside this skill. Tell the user this skill only produces optimized prompts, and instruct them to make a normal task request if they want execution instead.

Run this 6-phase pipeline sequentially. Present results using the Output Format below.

Analysis Pipeline

Phase 0: Project Detection

Before analyzing the prompt, detect the current project context:

  1. Check if a CLAUDE.md exists in the working directory — read it for project conventions
  2. Detect tech stack from project files:
    • package.json → Node.js / TypeScript / React / Next.js
    • go.mod → Go
    • pyproject.toml / requirements.txt → Python
    • Cargo.toml → Rust
    • build.gradle / pom.xml → Java / Kotlin (then check for quarkus in build file → Quarkus, or spring-boot → Spring Boot)
    • Package.swift → Swift
    • Gemfile → Ruby
    • composer.json → PHP
    • *.csproj / *.sln → .NET
    • Makefile / CMakeLists.txt → C / C++
    • cpanfile / Makefile.PL → Perl
  3. Note detected tech stack for use in Phase 3 and Phase 4

If no project files are found (e.g., the prompt is abstract or for a new project), skip detection and flag "tech stack unknown" in Phase 4.

Phase 1: Intent Detection

Classify the user's task into one or more categories:

CategorySignal WordsExample
New Featurebuild, create, add, implement, , ,"Build a login page"
Bug Fixfix, broken, not working, error, ,"Fix the auth flow"
Refactorrefactor, clean up, restructure, ,"Refactor the API layer"
Researchhow to, what is, explore, investigate, ,"How to add SSO"
Testingtest, coverage, verify, ,"Add tests for the cart"
Reviewreview, audit, check, ,"Review my PR"
Documentationdocument, update docs,"Update the API docs"
Infrastructuredeploy, CI, docker, database, ,"Set up CI/CD pipeline"
Designdesign, architecture, plan, ,"Design the data model"

Phase 2: Scope Assessment

If Phase 0 detected a project, use codebase size as a signal. Otherwise, estimate from the prompt description alone and mark the estimate as uncertain.

ScopeHeuristicOrchestration
TRIVIALSingle file, < 50 linesDirect execution
LOWSingle component or moduleSingle command or skill
MEDIUMMultiple components, same domainCommand chain + /verify
HIGHCross-domain, 5+ files/plan first, then phased execution
EPICMulti-session, multi-PR, architectural shiftUse blueprint skill for multi-session plan

Phase 3: SKC Component Matching

Map intent + scope + tech stack (from Phase 0) to specific SKC components.

By Intent Type

IntentCommandsSkillsAgents
New Feature/plan, /tdd, /code-review, /verifytdd-workflow, verification-loopplanner, tdd-guide, code-reviewer
Bug Fix/tdd, /build-fix, /verifytdd-workflowtdd-guide, build-error-resolver
Refactor/refactor-clean, /code-review, /verifyverification-looprefactor-cleaner, code-reviewer
Research/plansearch-first, iterative-retrieval
Testing/tdd, /e2e, /test-coveragetdd-workflow, e2e-testingtdd-guide, e2e-runner
Review/code-reviewsecurity-reviewcode-reviewer, security-reviewer
Documentation/update-docs, /update-codemapsdoc-updater
Infrastructure/plan, /verifydocker-patterns, deployment-patterns, database-migrationsarchitect
Design (MEDIUM-HIGH)/planplanner, architect
Design (EPIC)blueprint (invoke as skill)planner, architect

By Tech Stack

Tech StackSkills to AddAgent
Python / Djangodjango-patterns, django-tdd, django-security, django-verification, python-patterns, python-testingpython-reviewer
Gogolang-patterns, golang-testinggo-reviewer, go-build-resolver
Spring Boot / Javaspringboot-patterns, springboot-tdd, springboot-security, springboot-verification, java-coding-standards, jpa-patternsjava-reviewer
Quarkus / Javaquarkus-patterns, quarkus-tdd, quarkus-security, quarkus-verification, java-coding-standards, jpa-patternsjava-reviewer
Kotlin / Androidkotlin-coroutines-flows, compose-multiplatform-patterns, android-clean-architecturekotlin-reviewer
TypeScript / Reactfrontend-patterns, backend-patterns, coding-standardscode-reviewer
Swift / iOSswiftui-patterns, swift-concurrency-6-2, swift-actor-persistence, swift-protocol-di-testingcode-reviewer
PostgreSQLpostgres-patterns, database-migrationsdatabase-reviewer
Perlperl-patterns, perl-testing, perl-securitycode-reviewer
C++cpp-coding-standards, cpp-testingcode-reviewer
Other / Unlistedcoding-standards (universal)code-reviewer

Phase 4: Missing Context Detection

Scan the prompt for missing critical information. Check each item and mark whether Phase 0 auto-detected it or the user must supply it:

  • Tech stack — Detected in Phase 0, or must user specify?
  • Target scope — Files, directories, or modules mentioned?
  • Acceptance criteria — How to know the task is done?
  • Error handling — Edge cases and failure modes addressed?
  • Security requirements — Auth, input validation, secrets?
  • Testing expectations — Unit, integration, E2E?
  • Performance constraints — Load, latency, resource limits?
  • UI/UX requirements — Design specs, responsive, a11y? (if frontend)
  • Database changes — Schema, migrations, indexes? (if data layer)
  • Existing patterns — Reference files or conventions to follow?
  • Scope boundaries — What NOT to do?

If 3+ critical items are missing, ask the user up to 3 clarification questions before generating the optimized prompt. Then incorporate the answers into the optimized prompt.

Phase 5: Workflow & Model Recommendation

Determine where this prompt sits in the development lifecycle:

Research → Plan → Implement (TDD) → Review → Verify → Commit

For MEDIUM+ tasks, always start with /plan. For EPIC tasks, use blueprint skill.

Model recommendation (include in output):

ScopeRecommended ModelRationale
TRIVIAL-LOWSonnet 4.6Fast, cost-efficient for simple tasks
MEDIUMSonnet 4.6Best coding model for standard work
HIGHSonnet 4.6 (main) + Opus 4.6 (planning)Opus for architecture, Sonnet for implementation
EPICOpus 4.6 (blueprint) + Sonnet 4.6 (execution)Deep reasoning for multi-session planning

Multi-prompt splitting (for HIGH/EPIC scope):

For tasks that exceed a single session, split into sequential prompts:

  • Prompt 1: Research + Plan (use search-first skill, then /plan)
  • Prompt 2-N: Implement one phase per prompt (each ends with /verify)
  • Final Prompt: Integration test + /code-review across all phases
  • Use /save-session and /resume-session to preserve context between sessions

Output Format

Present your analysis in this exact structure. Respond in the same language as the user's input.

Section 1: Prompt Diagnosis

Strengths: List what the original prompt does well.

Issues:

IssueImpactSuggested Fix
(problem)(consequence)(how to fix)

Needs Clarification: Numbered list of questions the user should answer. If Phase 0 auto-detected the answer, state it instead of asking.

Section 2: Recommended SKC Components

TypeComponentPurpose
Command/planPlan architecture before coding
Skilltdd-workflowTDD methodology guidance
Agentcode-reviewerPost-implementation review
ModelSonnet 4.6Recommended for this scope

Section 3: Optimized Prompt — Full Version

Present the complete optimized prompt inside a single fenced code block. The prompt must be self-contained and ready to copy-paste. Include:

  • Clear task description with context
  • Tech stack (detected or specified)
  • /command invocations at the right workflow stages
  • Acceptance criteria
  • Verification steps
  • Scope boundaries (what NOT to do)

For items that reference blueprint, write: "Use the blueprint skill to..." (not /blueprint, since blueprint is a skill, not a command).

Section 4: Optimized Prompt — Quick Version

A compact version for experienced SKC users. Vary by intent type:

IntentQuick Pattern
New Feature/plan [feature]. /tdd to implement. /code-review. /verify.
Bug Fix/tdd — write failing test for [bug]. Fix to green. /verify.
Refactor/refactor-clean [scope]. /code-review. /verify.
ResearchUse search-first skill for [topic]. /plan based on findings.
Testing/tdd [module]. /e2e for critical flows. /test-coverage.
Review/code-review. Then use security-reviewer agent.
Docs/update-docs. /update-codemaps.
EPICUse blueprint skill for "[objective]". Execute phases with /verify gates.

Section 5: Enhancement Rationale

EnhancementReason
(what was added)(why it matters)

Footer

Not what you need? Tell me what to adjust, or make a normal task request if you want execution instead of prompt optimization.


Examples

Trigger Examples

  • "Optimize this prompt for SKC"
  • "Rewrite this prompt so Claude Code uses the right commands"
  • "How should I prompt SKC for this task?"

Example 1: Vague Chinese Prompt (Project Detected)

User input:

Phase 0 detects: package.json with Next.js 15, TypeScript, Tailwind CSS

Optimized Prompt (Full):

(Next.js 15 + TypeScript + Tailwind CSS)


4. /code-review

- (rate limiting)
- XSS
- CSRF token


Example 2: Moderate English Prompt

User input:

Add a REST API endpoint for user profile updates with validation

Phase 0 detects: go.mod with Go 1.22, Chi router

Optimized Prompt (Full):

Add a REST API endpoint for user profile updates (PATCH /api/users/:id).

Tech stack: Go 1.22 + Chi router (detected from project)

Requirements:
- PATCH /api/users/:id — partial update of user profile
- Input validation for fields: name, email, avatar_url, bio
- Auth: require valid token, users can only update own profile
- Return 200 with updated user on success
- Return 400 with validation errors on invalid input
- Return 401/403 for auth failures
- Follow existing API patterns in the codebase

Workflow:
1. /plan the endpoint structure, middleware chain, and validation logic
2. /tdd — write table-driven tests for success, validation failure, auth failure, not-found
3. Implement following existing handler patterns
4. /go-review
5. /verify — run full test suite, confirm no regressions

Do not:
- Modify existing endpoints
- Change the database schema (use existing user table)
- Add new dependencies without checking existing ones first (use search-first skill)

Example 3: EPIC Project

User input:

Migrate our monolith to microservices

Optimized Prompt (Full):

Use the blueprint skill to plan: "Migrate monolith to microservices architecture"

Before executing, answer these questions in the blueprint:
1. Which domain boundaries exist in the current monolith?
2. Which service should be extracted first (lowest coupling)?
3. Communication pattern: REST APIs, gRPC, or event-driven (Kafka/RabbitMQ)?
4. Database strategy: shared DB initially or database-per-service from start?
5. Deployment target: Kubernetes, Docker Compose, or serverless?

The blueprint should produce phases like:
- Phase 1: Identify service boundaries and create domain map
- Phase 2: Set up infrastructure (API gateway, service mesh, CI/CD per service)
- Phase 3: Extract first service (strangler fig pattern)
- Phase 4: Verify with integration tests, then extract next service
- Phase N: Decommission monolith

Each phase = 1 PR, with /verify gates between phases.
Use /save-session between phases. Use /resume-session to continue.
Use git worktrees for parallel service extraction when dependencies allow.

Recommended: Opus 4.6 for blueprint planning, Sonnet 4.6 for phase execution.

Related Components

ComponentWhen to Reference
configure-eccUser hasn't set up SKC yet
skill-stocktakeAudit which components are installed (use instead of hardcoded catalog)
search-firstResearch phase in optimized prompts
blueprintEPIC-scope optimized prompts (invoke as skill, not command)
strategic-compactLong session context management
cost-aware-llm-pipelineToken optimization recommendations

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.