本文へ移動
cccskills
無料GitHub で公開

prompt-improver

Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Prompt Improver

Optimize code-related prompts for clarity, investigation-first thinking, and verification.

Input

$ARGUMENTS

Step 1: Analyze the Prompt

Evaluate the input prompt across these dimensions:

DimensionWhat to check
Task ClarityIs the task type clear? (implement, fix, refactor, review, test) Are boundaries defined?
InvestigationDoes it specify reading/understanding before acting?
VerificationAre there appropriate checks? (run tests, build, lint)
Context AnchoringDoes it reference specific files, functions, or patterns?
Action SpecificityIs the desired outcome explicit? Quality expectations stated?
Scope ControlIs it appropriately scoped? Clear stopping points?

Identify which dimensions are weak or missing in the input prompt.

Gates (sequenced)

Complete in order; do not skip steps.

  1. Audit gate (end of Step 1): Pass when the forthcoming Analysis names the task type and either lists each weak or missing dimension from the table or explicitly states all dimensions are adequate, with a brief reason for any dimension you treat as already sufficient.
  2. Transform gate (Step 2): Pass when every improvement you will list under "Improvements Applied" maps to a gap from the audit (or to deliberate strengthening when the prompt was already adequate).
  3. Output gate (Step 3): Pass when the Optimized Prompt block appears only after Gates 1–2 pass.

Step 2: Apply Transformation Rules

Task Clarity

  • Convert vague requests → specific task type + scope
  • Add "implement", "fix", "refactor", "review", or "test" when ambiguous
  • Specify affected files/components when inferable

Investigation-First

  • Add "Read and understand [relevant files] before making changes"
  • For bugs: "Reproduce and understand the root cause first"
  • For features: "Check existing patterns in the codebase"

Anti-Hallucination

  • "Do not assume code structure you haven't read"
  • "Verify imports/dependencies exist before using them"
  • "Check for existing utilities before creating new ones"

Verification Steps

  • For implementation: "Run tests after changes"
  • For refactoring: "Ensure behavior is unchanged"
  • For fixes: "Verify the fix and check for regressions"

Positive Framing

  • Convert "don't break tests" → "ensure tests pass after changes"
  • Convert "don't over-engineer" → "implement the minimal solution"

Scope Control

  • Add explicit boundaries when missing
  • Break multi-part requests into phases if needed

Examples

Bug Fix

Original: "fix the login bug"

Optimized:
Fix the login bug. First, read the authentication-related files to understand
the current implementation. Reproduce the bug to confirm the issue. Identify
the root cause before making changes. Implement the fix and run existing tests
to verify no regressions.

Feature Implementation

Original: "add dark mode"

Optimized:
Implement dark mode for the application. First, check how theming/styling is
currently handled in the codebase. Look for existing color variables or theme
patterns. Implement the toggle and theme switching with minimal changes to
existing components. Run tests and verify the UI renders correctly in both modes.

Refactoring

Original: "clean up the user service"

Optimized:
Refactor the user service for improved maintainability. Read the current
implementation and understand its responsibilities. Identify specific issues
(duplication, unclear naming, tight coupling). Make incremental changes,
running tests after each step to ensure behavior is unchanged.

Task-Type Tips

Task TypeTip
Bug fixesInclude reproduction steps if known. Specify whether this is a quick fix or needs root cause analysis.
Feature implementationReference similar features in the codebase. Specify if tests are expected.
RefactoringState the goal (readability, performance, testability). Emphasize incremental changes.
Code reviewSpecify focus areas (security, performance, style). Mention what to ignore.
TestingSpecify test type (unit, integration, e2e). Reference existing test patterns.

Step 3: Generate Output

Follow the Gates under Step 1 (audit → transform → output). Produce output in this exact format:

Analysis

[2-3 sentences identifying the prompt type, which dimensions are weak or missing, or why all dimensions are already adequate]

Improvements Applied

  • [Bullet list of specific transformations applied]

Optimized Prompt

[The improved prompt, ready to copy and use]

Tips for This Prompt Type

[1-2 sentences of relevant tips from the Task-Type Tips table]

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Reviews App Intents code for intent structure, entities, shortcuts, and parameters. Use when reviewing code with import AppIntents, @AppIntent, AppEntity, AppShortcutsProvider, or @Parameter.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

existential-birds のスキルをすべて見る

このスキルの問題を報告する