Orca CLI
stablyai/orca
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…
Transform messy prompts into well-structured, effective prompts — single or multi-agent.
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills reprompter --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/reprompter .claude/skills/reprompter && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .claude/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompterType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills reprompter --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/reprompter .agents/skills/reprompter && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .agents/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills reprompter --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/reprompter .cursor/skills/reprompter && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .cursor/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/reprompter--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills reprompter --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/reprompter .gemini/skills/reprompter && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .gemini/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills reprompterInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/reprompter .github/skills/reprompter && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .github/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills reprompter --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/reprompter .opencode/skills/reprompter && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "reprompter" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/reprompter into .opencode/skills/reprompter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "reprompter", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
reprompterTransform messy prompts into well-structured, effective prompts — single or multi-agent.
Reprompter is an agent skill from LeoYeAI/openclaw-master-skills. Transform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured…
Its SKILL.md is about 5.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 28 other files, including scripts and assets (for example `CHANGELOG.md`, `CONTRIBUTING.md` and `README.md`). Compatibility notes: Single mode works on all Claude surfaces (Claude.ai, Claude Code, API). Repromptception mode requires Claude Code with tmux and…
It sits in Agent Workflows, covering Multi-agent orchestration. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
claudeFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.anthropic.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Single mode works on all Claude surfaces (Claude.ai, Claude Code, API). Repromptception mode requires Claude Code with tmux and CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.
From compatibility in the SKILL.md frontmatter.
Reprompter loads about 5.1k tokens when it runs. Until then it costs about 203 tokens; SKILL.md has 1,964 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,964 words, ~5,134 tokens.
.claude/skills/reprompter/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.Your prompt sucks. Let's fix that. Single prompts or full agent teams — one skill, two modes.
| Mode | Trigger | What 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.
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.
AskUserQuestion with clickable options (2-5 questions max)After interview completes, IMMEDIATELY:
❌ WRONG: Ask interview questions → stop
✅ RIGHT: Ask interview questions → generate prompt → show score → offer to executeAsk via AskUserQuestion. Max 5 questions total.
Standard questions (priority order — drop lower ones if task-specific questions are needed):
Task-specific questions (MANDATORY for compound prompts — replace lower-priority standard questions):
| Signal | Suggested mode |
|---|---|
| 2+ distinct systems (e.g., frontend + backend, API + DB, mobile + backend) | Team (Parallel) |
| Pipeline (fetch → transform → deploy) | Team (Sequential) |
| Single file/component | Single Agent |
| "audit", "review", "analyze" across areas | Team (Parallel) |
Enable when ALL true:
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).
Detect task type from input. Each type has a dedicated template in docs/references/:
| Type | Template | Use when |
|---|---|---|
| Feature | feature-template.md | New functionality (default fallback) |
| Bugfix | bugfix-template.md | Debug + fix |
| Refactor | refactor-template.md | Structural cleanup |
| Testing | testing-template.md | Test writing |
| API | api-template.md | Endpoint/API work |
| UI | ui-template.md | UI components |
| Security | security-template.md | Security audit/hardening |
| Docs | docs-template.md | Documentation |
| Content | content-template.md | Blog posts, articles, marketing copy |
| Research | research-template.md | Analysis/exploration |
| Multi-Agent | swarm-template.md | Multi-agent coordination |
| Team Brief | team-brief-template.md | Team 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.mdfollowing the XML structure below, then add it to the table above.
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>Auto-detect tech stack from current working directory ONLY:
package.json, tsconfig.json, prisma/schema.prisma, etc.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.
/tmp/rpt-brief-{taskname}.md (use unique tasknames to avoid collisions between concurrent runs)For EACH agent:
docs/references/ (or use base XML structure)<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 pathsScore each prompt — target 8+/10. If under 8, add more context/constraints.
Write all to /tmp/rpt-agent-prompts-{taskname}.md
# 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}⚠️ WARNING: Default teammate model is HAIKU unless explicitly overridden. Always set --model opus in both CLI launch command and team prompt.
| Rule | Why |
|---|---|
Always send-keys -l (literal flag) | Without it, special chars break |
| Enter sent SEPARATELY | Combined fails for multiline |
| sleep 0.5 between text and Enter | Buffer processing time |
| sleep 12 after session start | Claude Code init time |
--model opus in CLI AND prompt | Default teammate = HAIKU |
| Each agent writes own file | Prevents file conflicts |
| Unique taskname per run | Prevents collisions between concurrent sessions |
Read each agent's report
Score against success criteria from Phase 2:
Accept checklist (use alongside score — all must pass):
Max 2 retries (3 total attempts)
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.| Team size | Time | Cost |
|---|---|---|
| 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.
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.
Always show before/after metrics:
| Dimension | Weight | Criteria |
|---|---|---|
| Clarity | 20% | Task unambiguous? |
| Specificity | 20% | Requirements concrete? |
| Structure | 15% | Proper sections, logical flow? |
| Constraints | 15% | Boundaries defined? |
| Verifiability | 15% | Success measurable? |
| Decomposition | 15% | 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.
For both modes, RePrompter supports post-execution evaluation:
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: ..."
Prefill assistant response start to enforce format:
{ → forces JSON output## Analysis → skips preamble, starts with content| Column | → forces table formatGenerated prompts should COMPLEMENT runtime context (CLAUDE.md, skills, MCP tools), not duplicate it. Before generating:
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.
Always include explicit permission for the model to express uncertainty rather than fabricate:
Note:
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMSis 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"
}
}| Setting | Values | Effect |
|---|---|---|
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. |
Rough crypto dashboard prompt: 1.6/10 → 9.0/10 (+462%)
3 Opus agents, sequential pipeline (PromptAnalyzer → PromptEngineer → QualityAuditor):
| Metric | Value |
|---|---|
| Original score | 2.15/10 |
| After Repromptception | 9.15/10 (+326%) |
| Quality audit | PASS (99.1%) |
| Weaknesses found → fixed | 24/24 (100%) |
| Cost | $1.39 |
| Time | ~8 minutes |
Same audit task, 4 Opus agents:
| Metric | Raw | Repromptception | Delta |
|---|---|---|---|
| CRITICAL findings | 7 | 14 | +100% |
| Total findings | ~40 | 104 | +160% |
| Cost savings identified | $377/mo | $490/mo | +30% |
| Token bloat found | 45K | 113K | +151% |
| Cross-validated findings | 0 | 5 | — |
See TESTING.md for 13 verification scenarios + anti-pattern examples.
Templates may add domain-specific tags beyond the 8 required base tags. Always include all base tags first.
| Extended Tag | Used In | Purpose |
|---|---|---|
<symptoms> | bugfix | What the user sees, error messages |
<investigation_steps> | bugfix | Systematic debugging steps |
<endpoints> | api | Endpoint specifications |
<component_spec> | ui | Component props, states, layout |
<agents> | swarm | Agent role definitions |
<task_decomposition> | swarm | Work split per agent |
<coordination> | swarm | Inter-agent handoff rules |
<research_questions> | research | Specific questions to answer |
<methodology> | research | Research approach and methods |
<reasoning> | research | Reasoning notes space (non-sensitive, concise) |
<current_state> | refactor | Before state of the code |
<target_state> | refactor | Desired after state |
<coverage_requirements> | testing | What needs test coverage |
<threat_model> | security | Threat landscape and vectors |
<structure> | docs | Document organization |
<reference> | docs | Source material to reference |
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 25 other files (scripts, assets) in skills/reprompter of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Reprompter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Reprompter this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~5.1k | Automated safety check: Pass | MIT | |
| Orca CLIstablyai/orca | 87k | 2 repos | ~593 | Automated safety check: Pass | MIT | |
| Paseo Advisor Second Opiniongetpaseo/paseo | 20k | 1 repos | ~756 | Automated safety check: Pass | Custom licence | |
| O2 Review Loopopenobserve/openobserve | 22k | — | ~3.7k | Automated safety check: Pass | AGPL-3.0 | |
| Paseo Committeegetpaseo/paseo | 20k | 1 repos | ~496 | Automated safety check: Pass | Custom licence | |
| Mission Control Agent APIbuilderz-labs/mission-control | 6.3k | — | ~2.1k | Automated safety check: Pass | MIT |
stablyai/orca
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…
getpaseo/paseo
Launches one separate agent through Paseo to give a second opinion on the current task, with a self-contained briefing and no permission to edit files.
openobserve/openobserve
Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.
getpaseo/paseo
Forms a two-agent committee with contrasting profiles to analyze a stuck problem in parallel, reconcile their views and return a consensus plan without editing files.
builderz-labs/mission-control
Teaches an agent to use the Mission Control dashboard API: register, send heartbeats, fetch assigned tasks, report progress and disconnect, with API key auth.
getpaseo/paseo
Hands off the current task, including context, decisions and failed attempts, to a fresh agent through Paseo by writing a self-contained briefing prompt and launching that agent.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Categories
Transform messy prompts into well-structured, effective prompts — single or multi-agent. Reprompter is an agent skill from LeoYeAI/openclaw-master-skills. Transform messy prompts into well-structured, effective prompts — single or multi-agent.
Reprompter fits situations like: clean up this prompt; structure my prompt; rough text needing XML tags and best practices; reprompter teams.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a claude-code`. Or copy the skill folder (skills/reprompter in LeoYeAI/openclaw-master-skills) into .claude/skills/reprompter in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a codex`. Or copy the skill folder (skills/reprompter in LeoYeAI/openclaw-master-skills) into .agents/skills/reprompter in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill reprompter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reprompter, .gemini/skills/reprompter, .github/skills/reprompter and .opencode/skills/reprompter in your project.
Going by SKILL.md and its folder, Reprompter needs the command-line tools its instructions call (claude). Compatibility (from SKILL.md): Single mode works on all Claude surfaces (Claude.ai, Claude Code, API). Repromptception mode requires Claude Code with tmux and CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1. .
SKILL.md names 1 domain. As links in the text: docs.anthropic.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Reprompter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.1k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Reprompter: Orca CLI (stablyai/orca, 87k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,158 GitHub stars. The repository holds 1,215 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.