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Agent Skill · 研究检索

reprompter(reprompter 搜索)

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

reprompter 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 5,744。

总安装 24,531周安装 1,043GitHub Stars 公开资料未说明下载 8,594核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
7.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reprompter(reprompter 搜索)
来源仓库:https://github.com/aytuncyildizli/reprompter
安装命令:
openclaw skills install reprompter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install reprompter

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

优化提示词结构,使其更适合单代理或多代理协作场景。支持清理杂乱指令、拆分复杂任务和统一输出格式。提升提示词可复用性,降低沟通成本。reprompter 属于研究检索类 Skill,可作为该场景下的辅助能力补充。不建议将示例直接作为硬规则使用,应结合实际业务调整。涉及自动执行时,务必在提示中明确确认步骤与失败回退机制。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install reprompter;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

reprompter 怎么安装?

在终端执行 openclaw skills install reprompter。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

reprompter 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

reprompter 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

reprompter 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

reprompter 用的人多吗?

公开数据是累计安装 24,531 次,近一周安装 1,043 次,下载 8,594 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

reprompter 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
reprompter

reprompter 搜索 · 本页

24,531—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
reprompter
description
|
Outputs
Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files.
compatibility
|
metadata
author
AytuncYildizli
version
7.0.0

RePrompter v7.0

Your prompt sucks. Let's fix that. Single prompts or full agent teams — one skill, two modes.

Two Modes

ModeTriggerWhat happens
Single"reprompt this", "clean up this prompt"Interview → structured prompt → score
Repromptception"reprompter teams", "repromptception", "run with quality", "smart run", "smart agents"Plan team → reprompt each agent → tmux Agent Teams → evaluate → retry

Auto-detection: if task mentions 2+ systems, "audit", or "parallel" → ask: "This looks like a multi-agent task. Want to use Repromptception mode?"

Definition — 2+ systems means at least two distinct technical domains that can be worked independently. Examples: frontend + backend, API + database, mobile app + backend, infrastructure + application code, security audit + cost audit.

Don't Use When

  • User wants a simple direct answer (no prompt generation needed)
  • User wants casual chat/conversation
  • Task is immediate execution-only with no reprompting step
  • Scope does not involve prompt design, structure, or orchestration
Clarification: RePrompter does support code-related tasks (feature, bugfix, API, refactor) by generating better prompts. It does not directly apply code changes in Single mode. Direct code execution belongs to coding-agent unless Repromptception execution mode is explicitly requested.

Mode 1: Single Prompt

Process

  1. Receive raw input
  2. Input guard — if input is empty, a single word with no verb, or clearly not a task → ask the user to describe what they want to accomplish

- Reject examples: "hi", "thanks", "lol", "what's up", "good morning", random emoji-only input - Accept examples: "fix login bug", "write API tests", "improve this prompt"

  1. Quick Mode gate — under 20 words, single action, no complexity indicators → generate immediately
  2. Smart Interview — use AskUserQuestion with clickable options (2-5 questions max)
  3. Generate + Score — apply template, show before/after quality metrics

⚠️ MUST GENERATE AFTER INTERVIEW

After interview completes, IMMEDIATELY:

  1. Select template based on task type
  2. Generate the full polished prompt
  3. Show quality score (before/after table)
  4. Ask if user wants to execute or copy
❌ WRONG: Ask interview questions → stop
✅ RIGHT: Ask interview questions → generate prompt → show score → offer to execute

Interview Questions

Ask via AskUserQuestion. Max 5 questions total.

Standard questions (priority order — drop lower ones if task-specific questions are needed):

  1. Task type: Build Feature / Fix Bug / Refactor / Write Tests / API Work / UI / Security / Docs / Content / Research / Multi-Agent

- If user selects Multi-Agent while currently in Single mode, immediately transition to Repromptception Phase 1 (Team Plan) and confirm team execution mode (Parallel vs Sequential).

  1. Execution mode: Single Agent / Team (Parallel) / Team (Sequential) / Let RePrompter decide
  2. Motivation: User-facing / Internal tooling / Bug fix / Exploration / Skip *(drop first if space needed)*
  3. Output format: XML Tags / Markdown / Plain Text / JSON *(drop first if space needed)*

Task-specific questions (MANDATORY for compound prompts — replace lower-priority standard questions):

  • Extract keywords from prompt → generate relevant follow-up options
  • Example: prompt mentions "telegram" → ask about alert type, interactivity, delivery
  • Vague prompt fallback: if input has no extractable keywords (e.g., "make it better"), ask open-ended: "What are you working on?" and "What's the goal?" before proceeding

Auto-Detect Complexity

SignalSuggested mode
2+ distinct systems (e.g., frontend + backend, API + DB, mobile + backend)Team (Parallel)
Pipeline (fetch → transform → deploy)Team (Sequential)
Single file/componentSingle Agent
"audit", "review", "analyze" across areasTeam (Parallel)

Quick Mode

Enable when ALL true:

  • < 20 words (excluding code blocks)
  • Exactly 1 action verb from: add, fix, remove, rename, move, delete, update, create, implement, write, change, configure, test, run
  • Single target (one file, component, or identifier)
  • No conjunctions (and, or, plus, also)
  • No vague modifiers (better, improved, some, maybe, kind of)

Force interview if ANY present: compound tasks ("and", "plus"), state management ("track", "sync"), vague modifiers ("better", "improved"), integration work ("connect", "combine", "sync"), broad scope nouns after any action verb, ambiguous pronouns ("it", "this", "that" without clear referent).

Task Types & Templates

Detect task type from input. Each type has a dedicated template in docs/references/:

TypeTemplateUse when
Featurefeature-template.mdNew functionality (default fallback)
Bugfixbugfix-template.mdDebug + fix
Refactorrefactor-template.mdStructural cleanup
Testingtesting-template.mdTest writing
APIapi-template.mdEndpoint/API work
UIui-template.mdUI components
Securitysecurity-template.mdSecurity audit/hardening
Docsdocs-template.mdDocumentation
Contentcontent-template.mdBlog posts, articles, marketing copy
Researchresearch-template.mdAnalysis/exploration
Multi-Agentswarm-template.mdMulti-agent coordination
Team Briefteam-brief-template.mdTeam orchestration brief

Priority (most specific wins): api > security > ui > testing > bugfix > refactor > content > docs > research > feature. For multi-agent tasks, use swarm-template for the team brief and the type-specific template for each agent's sub-prompt.

How it works: Read the matching template from docs/references/{type}-template.md, then fill it with task-specific context. Templates are NOT loaded into context by default — only read on demand when generating a prompt. If the template file is not found, fall back to the Base XML Structure below.

To add a new task type: create docs/references/{type}-template.md following the XML structure below, then add it to the table above.

Base XML Structure

All templates follow this core structure (8 required tags). Use as fallback if no specific template matches:

Exception: team-brief-template.md uses Markdown format for orchestration briefs. This is intentional — see template header for rationale.

<role>{Expert role matching task type and domain}</role>

<context>
- Working environment, frameworks, tools
- Available resources, current state
</context>

<task>{Clear, unambiguous single-sentence task}</task>

<motivation>{Why this matters — priority, impact}</motivation>

<requirements>
- {Specific, measurable requirement 1}
- {At least 3-5 requirements}
</requirements>

<constraints>
- {What NOT to do}
- {Boundaries and limits}
</constraints>

<output_format>{Expected format, structure, length}</output_format>

<success_criteria>
- {Testable condition 1}
- {Measurable outcome 2}
</success_criteria>

Project Context Detection

Auto-detect tech stack from current working directory ONLY:

  • Scan package.json, tsconfig.json, prisma/schema.prisma, etc.
  • Session-scoped — different directory = fresh context
  • Opt out with "no context", "generic", or "manual context"
  • Never scan parent directories or carry context between sessions

Mode 2: Repromptception (Agent Teams)

TL;DR

Raw task in → quality output out. Every agent gets a reprompted prompt.

Phase 1: Score raw prompt, plan team, define roles (YOU do this, ~30s)
Phase 2: Write XML-structured prompt per agent (YOU do this, ~2min)
Phase 3: Launch tmux Agent Teams (AUTOMATED)
Phase 4: Read results, score, retry if needed (YOU do this)

Key insight: The reprompt phase costs ZERO extra tokens — YOU write the prompts, not another AI.

Phase 1: Team Plan (~30 seconds)

  1. Score raw prompt (1-10): Clarity, Specificity, Structure, Constraints, Decomposition

- Phase 1 uses 5 quick-assessment dimensions. The full 6-dimension scoring (adding Verifiability) is used in Phase 4 evaluation.

  1. Pick mode: parallel (independent agents) or sequential (pipeline with dependencies)
  2. Define team: 2-5 agents max, each owns ONE domain, no overlap
  3. Write team brief to /tmp/rpt-brief-{taskname}.md (use unique tasknames to avoid collisions between concurrent runs)

Phase 2: Repromptception (~2 minutes)

For EACH agent:

  1. Pick the best-matching template from docs/references/ (or use base XML structure)
  2. Read it, then apply these per-agent adaptations:
  • <role>: Specific expert title for THIS agent's domain
  • <context>: Add exact file paths (verified with ls), what OTHER agents handle (boundary awareness)
  • <requirements>: At least 5 specific, independently verifiable requirements
  • <constraints>: Scope boundary with other agents, read-only vs write, file/directory boundaries
  • <output_format>: Exact path /tmp/rpt-{taskname}-{agent-domain}.md, required sections
  • <success_criteria>: Minimum N findings, file:line references, no hallucinated paths

Score each prompt — target 8+/10. If under 8, add more context/constraints.

Write all to /tmp/rpt-agent-prompts-{taskname}.md

Phase 3: Execute (tmux Agent Teams)

# 1. Start Claude Code with Agent Teams
tmux new-session -d -s {session} "cd /path/to/workdir && CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 claude --model opus"
# placeholders:
# - {session}: unique tmux session name (example: rpt-auth-audit)
# - /path/to/workdir: absolute repository path for the target project (example: /tmp/reprompter-check)

# 2. Wait for startup
sleep 12

# 3. Send prompt — MUST use -l (literal), Enter SEPARATE
# IMPORTANT: Include POLLING RULES to prevent lead TaskList loop bug
tmux send-keys -t {session} -l 'Create an agent team with N teammates. CRITICAL: Use model opus for ALL tasks.

POLLING RULES — YOU MUST FOLLOW THESE:
- After sending tasks, poll TaskList at most 10 times
- If ALL tasks show "done" status, IMMEDIATELY stop polling
- After 3 consecutive TaskList calls showing the same status, STOP polling regardless
- Once you stop polling: read the output files, then write synthesis
- DO NOT call TaskList more than 20 times total under any circumstances

Teammate 1 (ROLE): TASK. Write output to /tmp/rpt-{taskname}-{domain}.md. ... After all complete, synthesize into /tmp/rpt-{taskname}-final.md'
sleep 0.5
tmux send-keys -t {session} Enter

# 4. Monitor (poll every 15-30s)
tmux capture-pane -t {session} -p -S -100

# 5. Verify outputs
ls -la /tmp/rpt-{taskname}-*.md

# 6. Cleanup
tmux kill-session -t {session}
Critical tmux Rules

⚠️ WARNING: Default teammate model is HAIKU unless explicitly overridden. Always set --model opus in both CLI launch command and team prompt.

RuleWhy
Always send-keys -l (literal flag)Without it, special chars break
Enter sent SEPARATELYCombined fails for multiline
sleep 0.5 between text and EnterBuffer processing time
sleep 12 after session startClaude Code init time
--model opus in CLI AND promptDefault teammate = HAIKU
Each agent writes own filePrevents file conflicts
Unique taskname per runPrevents collisions between concurrent sessions

Phase 4: Evaluate + Retry

  1. Read each agent's report
  2. Score against success criteria from Phase 2:

- 8+/10 → ACCEPT - 4-6/10 → RETRY with delta prompt (tell them what's missing) - < 4/10 → RETRY with full rewrite

Accept checklist (use alongside score — all must pass): - [ ] All required output sections present - [ ] Requirements from Phase 2 independently verifiable - [ ] No hallucinated file paths or line numbers - [ ] Scope boundaries respected (no overlap with other agents)

  1. Max 2 retries (3 total attempts)
  2. Deliver final report to user

Delta prompt pattern:

Previous attempt scored 5/10.
✅ Good: Sections 1-3 complete
❌ Missing: Section 4 empty, line references wrong
This retry: Focus on gaps. Verify all line numbers.

Expected Cost & Time

Team sizeTimeCost
2 agents~5-8 min~$1-2
3 agents~8-12 min~$2-3
4 agents~10-15 min~$2-4

Estimates cover Phase 3 (execution) only. Add ~3 minutes for Phases 1-2 and ~5-8 minutes per retry. Each agent uses ~25-70% of their 200K token context window.

Fallback: sessions_spawn (OpenClaw only)

When tmux/Claude Code is unavailable but running inside OpenClaw:

sessions_spawn(task: "<per-agent prompt>", model: "opus", label: "rpt-{role}")

Note: sessions_spawn is an OpenClaw-specific tool. Not available in standalone Claude Code.

No tmux or OpenClaw? Run agents sequentially: execute each agent's prompt one at a time in the same Claude Code session. Slower but works everywhere.


Quality Scoring

Always show before/after metrics:

DimensionWeightCriteria
Clarity20%Task unambiguous?
Specificity20%Requirements concrete?
Structure15%Proper sections, logical flow?
Constraints15%Boundaries defined?
Verifiability15%Success measurable?
Decomposition15%Work split cleanly? (Score 10 if task is correctly atomic)
| Dimension | Before | After | Change |
|-----------|--------|-------|--------|
| Clarity | 3/10 | 9/10 | +200% |
| Specificity | 2/10 | 8/10 | +300% |
| Structure | 1/10 | 10/10 | +900% |
| Constraints | 0/10 | 7/10 | new |
| Verifiability | 2/10 | 8/10 | +300% |
| Decomposition | 0/10 | 8/10 | new |
| **Overall** | **1.45/10** | **8.35/10** | **+476%** |
Bias note: Scores are self-assessed. Treat as directional indicators, not absolutes.

Closed-Loop Quality (v6.0+)

For both modes, RePrompter supports post-execution evaluation:

  1. IMPROVE — Score raw → generate structured prompt
  2. EXECUTE — Repromptception mode only: route to agent(s), collect output. Single mode does not execute code/commands; it only generates prompts.
  3. EVALUATE — Score output/prompt against success criteria (0-10)
  4. RETRY — Thresholds: Single mode retry if score < 7; Repromptception retry if score < 8. Max 2 retries.

Advanced Features

Reasoning-Friendly Prompting (Claude 4.x)

Prompts should be less prescriptive about HOW. Focus on WHAT — clear task, requirements, constraints, success criteria. Let the model's own reasoning handle execution strategy.

Example: Instead of "Step 1: read the file, Step 2: extract the function" → "Extract the authentication logic from auth.ts into a reusable middleware. Requirements: ..."

Response Prefilling (API only)

Prefill assistant response start to enforce format:

  • { → forces JSON output
  • ## Analysis → skips preamble, starts with content
  • | Column | → forces table format

Context Engineering

Generated prompts should COMPLEMENT runtime context (CLAUDE.md, skills, MCP tools), not duplicate it. Before generating:

  1. Check what context is already loaded (project files, skills, MCP servers)
  2. Reference existing context: "Using the project structure from CLAUDE.md..."
  3. Add ONLY what's missing — avoid restating what the model already knows

Token Budget

Keep generated prompts under ~2K tokens for single mode, ~1K per agent for Repromptception. Longer prompts waste context window without improving quality. If a prompt exceeds budget, split into phases or move detail into constraints.

Uncertainty Handling

Always include explicit permission for the model to express uncertainty rather than fabricate:

  • Add to constraints: "If unsure about any requirement, ask for clarification rather than assuming"
  • For research tasks: "Clearly label confidence levels (high/medium/low) for each finding"
  • For code tasks: "Flag any assumptions about the codebase with TODO comments"

Settings (for Repromptception mode)

Note: CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS is an experimental flag that may change in future Claude Code versions. Check Claude Code docs for current status.

In ~/.claude/settings.json:

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  },
  "preferences": {
    "teammateMode": "tmux",
    "model": "opus"
  }
}
SettingValuesEffect
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS"1"Enables agent team spawning
teammateMode"tmux" / "default"tmux: each teammate gets a visible split pane. default: teammates run in background
model"opus" / "sonnet"Teammates default to Haiku. Always set model: opus explicitly in your prompt — do not rely on runtime defaults.

Proven Results

Single Prompt (v6.0)

Rough crypto dashboard prompt: 1.6/10 → 9.0/10 (+462%)

Repromptception E2E (v6.1)

3 Opus agents, sequential pipeline (PromptAnalyzer → PromptEngineer → QualityAuditor):

MetricValue
Original score2.15/10
After Repromptception9.15/10 (+326%)
Quality auditPASS (99.1%)
Weaknesses found → fixed24/24 (100%)
Cost$1.39
Time~8 minutes

Repromptception vs Raw Agent Teams (v7.0)

Same audit task, 4 Opus agents:

MetricRawRepromptceptionDelta
CRITICAL findings714+100%
Total findings~40104+160%
Cost savings identified$377/mo$490/mo+30%
Token bloat found45K113K+151%
Cross-validated findings05—

Tips

  • More context = fewer questions — mention tech stack, files
  • "expand" — if Quick Mode gave too simple a result, re-run with full interview
  • "quick" — skip interview for simple tasks
  • "no context" — skip auto-detection
  • Context is per-project — switching directories = fresh detection

Test Scenarios

See TESTING.md for 13 verification scenarios + anti-pattern examples.


Appendix: Extended XML Tags

Templates may add domain-specific tags beyond the 8 required base tags. Always include all base tags first.

Extended TagUsed InPurpose
<symptoms>bugfixWhat the user sees, error messages
<investigation_steps>bugfixSystematic debugging steps
<endpoints>apiEndpoint specifications
<component_spec>uiComponent props, states, layout
<agents>swarmAgent role definitions
<task_decomposition>swarmWork split per agent
<coordination>swarmInter-agent handoff rules
<research_questions>researchSpecific questions to answer
<methodology>researchResearch approach and methods
<reasoning>researchReasoning notes space (non-sensitive, concise)
<current_state>refactorBefore state of the code
<target_state>refactorDesired after state
<coverage_requirements>testingWhat needs test coverage
<threat_model>securityThreat landscape and vectors
<structure>docsDocument organization
<reference>docsSource material to reference

平台分布

OpenClaw

97.82%
按下载量换算8,407

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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