Money Ops
iamzifei/show-me-the-money
24/7 autonomous business operations orchestrator with business health scoring, canary monitoring, and safety guardrails.
Client onboarding and business diagnostic framework for AI agent deployments.
$ npx skills add LeoYeAI/openclaw-master-skills --skill client-onboarding-agent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills client-onboarding-agent --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/client-onboarding-agent .claude/skills/client-onboarding-agent && 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 "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .claude/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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/client-onboarding-agentType 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 client-onboarding-agent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills client-onboarding-agent --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/client-onboarding-agent .agents/skills/client-onboarding-agent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .agents/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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 client-onboarding-agent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills client-onboarding-agent --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/client-onboarding-agent .cursor/skills/client-onboarding-agent && 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 "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .cursor/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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/client-onboarding-agent--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 client-onboarding-agent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills client-onboarding-agent --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/client-onboarding-agent .gemini/skills/client-onboarding-agent && 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 "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .gemini/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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 client-onboarding-agentInstalls 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 client-onboarding-agent -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/client-onboarding-agent .github/skills/client-onboarding-agent && 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 "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .github/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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 client-onboarding-agent -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 client-onboarding-agent --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/client-onboarding-agent .opencode/skills/client-onboarding-agent && 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 "client-onboarding-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/client-onboarding-agent into .opencode/skills/client-onboarding-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-onboarding-agent", 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.
client-onboarding-agentClient onboarding and business diagnostic framework for AI agent deployments.
Client Onboarding Agent is an agent skill from LeoYeAI/openclaw-master-skills. Client onboarding and business diagnostic framework for AI agent deployments. Covers 4-round diagnostic process, 6 constraint categories, deployment SOP with completion contracts, tiered advisory mode for new automations, and the 6-week sell narrative. Use when onboarding new clients for agent deployments or managed automation services. NOT for self-service SaaS onboarding or consumer products.
Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).
It sits in DevOps & Cloud, covering Deployment, Codebase knowledge for agents and Help center and FAQ content. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
6 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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From 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.
Client Onboarding Agent loads about 4.5k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 1,616 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); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,616 words, ~4,461 tokens.
.claude/skills/client-onboarding-agent/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Framework for onboarding new clients into AI agent deployments. This isn't a sales process — it's a diagnostic process. You're figuring out what's broken, what can be automated, and what the constraints are before you promise anything.
Every client engagement starts with four rounds of structured discovery. Each round has a specific purpose and produces a specific artifact. Do not skip rounds. Do not combine rounds.
Purpose: Understand what hurts and what they're already using.
Duration: 30-60 minutes
Questions to ask:
What you're listening for:
Artifact: Pain Point Map
## Pain Point Map — [Client Name]
Date: [Date]
### Critical Pain Points (daily impact)
1. [Pain point]: Currently handled by [who] using [tool]. Takes [time].
2. [Pain point]: Currently handled by [who] using [tool]. Takes [time].
### Significant Pain Points (weekly impact)
1. [Pain point]: Currently handled by [who] using [tool]. Takes [time].
### Chronic Pain Points (ongoing frustration)
1. [Pain point]: No current solution / workaround is [description].
### Current Tool Stack
- [Tool 1]: Used for [purpose]. Satisfaction: [1-5]
- [Tool 2]: Used for [purpose]. Satisfaction: [1-5]
- [Tool 3]: Used for [purpose]. Satisfaction: [1-5]Purpose: Map how information actually flows through the business. Not the org chart — the real flow.
Duration: 45-90 minutes
Questions to ask:
What you're mapping:
Artifact: Workflow Diagram
[Trigger] → [Step 1: who/tool] → [Decision?] → [Step 2: who/tool] → [Output]
↓
[Alternative path]Create one diagram per major workflow. Mark each step with:
Purpose: Identify what will block or limit the deployment. This is where most onboardings fail — people skip constraint analysis and then hit walls during implementation.
Duration: 30-60 minutes
The 6 Constraint Categories:
Questions:
Questions:
Questions:
Questions:
Questions:
Questions:
Artifact: Constraint Matrix
## Constraint Matrix — [Client Name]
| Category | Constraint | Severity | Mitigation |
|----------------|-----------------------------------|----------|-------------------------------|
| Technical | Legacy payroll system, no API | High | Manual bridge or CSV export |
| Financial | $X/month max budget | Medium | Prioritize highest-ROI agents |
| Regulatory | HIPAA applies to patient data | High | On-premise only, no cloud API |
| Organizational | Owner travels 2 weeks/month | Medium | Async onboarding + mobile |
| Data | 3 years of data in spreadsheets | Low | One-time import project |
| Timeline | Tax season starts in 8 weeks | High | Deploy accounting agent first |Purpose: Based on Rounds 1-3, design the actual deployment plan and prioritize what gets built first.
Duration: 60-90 minutes (may include follow-up)
Process:
Match pain points to automatable workflows
Prioritize by score
Design the deployment plan
Set completion contracts for each phase (see below)
Artifact: Deployment Plan
## Deployment Plan — [Client Name]
### Phase 1 (Weeks 1-2): [Name of automation]
- Pain point addressed: [from Round 1]
- Workflow automated: [from Round 2]
- Constraints mitigated: [from Round 3]
- Completion contract: [see below]
- Expected impact: [specific, measurable]
### Phase 2 (Weeks 3-4): [Name of automation]
[Same structure]
### Phase 3 (Weeks 5-6): [Name of automation]
[Same structure]Every deliverable gets a completion contract. No ambiguity. No "it's mostly done." Done is binary.
## Completion Contract: [Deliverable Name]
### Done Criteria (ALL must be true)
1. [Specific, observable criterion]
2. [Specific, observable criterion]
3. [Specific, observable criterion]
### Observable Evidence
- [ ] [What you can see/verify to confirm criterion 1]
- [ ] [What you can see/verify to confirm criterion 2]
- [ ] [What you can see/verify to confirm criterion 3]
### Staged Approval
- Stage 1: Internal verification (we confirm it works)
- Stage 2: Client demo (client sees it work)
- Stage 3: Client independent use (client uses it without help)
### Timeout Bounds
- Expected completion: [date]
- Hard deadline: [date]
- If not complete by hard deadline: [what happens — usually rescope]## Completion Contract: Automated Invoice Processing
### Done Criteria
1. Agent can read incoming invoices from email attachments (PDF, image)
2. Agent correctly extracts vendor, amount, date, and line items with >95% accuracy
3. Agent creates corresponding entry in QuickBooks with correct categorization
4. Agent flags anomalies (unusual amounts, new vendors) for human review
### Observable Evidence
- [ ] Process 20 test invoices with known correct values; >19 match
- [ ] New vendor triggers human review notification (test with 3 new vendors)
- [ ] QuickBooks entries match invoice data exactly (spot-check 10)
- [ ] Agent handles unreadable invoices gracefully (flags, doesn't guess)
### Staged Approval
- Stage 1: We process 50 historical invoices, verify accuracy
- Stage 2: Client watches live processing of 5 real invoices
- Stage 3: Client runs independently for 5 business days, reports issues
### Timeout Bounds
- Expected: 10 business days from deployment start
- Hard deadline: 15 business days
- If missed: Rescope to manual-assist mode (agent prepares, human confirms)Not all automations are created equal. Some are safe to let the agent run unsupervised. Some should never run without a human in the loop. Use this tiering system for every automation.
| Tier | Risk Level | Supervision | Promotion Timeline | Example |
|---|---|---|---|---|
| Low | Low risk, easily reversible | Self-promote after 3 days of clean operation | 3 days | Email sorting, report generation, data lookups |
| Medium | Moderate risk, some consequences | Human approves each action for 2 weeks, then auto with audit log | 2 weeks | Invoice processing, appointment scheduling, client communications |
| High | High risk, significant consequences | Human approves for minimum 2 weeks, never fully unsupervised | 2 weeks minimum, always monitored | Financial transactions, legal documents, compliance filings |
| Restricted | Critical risk, irreversible consequences | Always draft-only, human executes | Never promotes | Tax filings, wire transfers, contract signing, regulatory submissions |
Low tier promotion (3 days):
Day 1-3: Agent performs task, human reviews every output
Day 4: If zero errors → agent runs autonomously with daily summary
If any errors → reset counter, fix issue, restart 3-day windowMedium tier promotion (2 weeks):
Week 1: Agent prepares action, human approves before execution
Week 2: Same, with audit log review at end of each day
Week 3+: Agent executes autonomously, human reviews audit log daily
If any error at any stage → drop back to full approval modeHigh tier (never fully autonomous):
Week 1-2: Agent prepares, human approves every action
Week 3+: Agent prepares, human approves every action
Always: Human spot-checks are mandatory, not optional
Frequency of checks can decrease but never reach zeroRestricted tier (always draft-only):
Always: Agent prepares draft/recommendation
Always: Human reviews, modifies if needed, and executes
Agent never has credentials/access to execute directlyDuring Round 4 (Solution Design), assign a tier to every automation:
| Automation | Tier | Rationale |
|-----------|------|-----------|
| Email triage | Low | Easily reversible, low consequences |
| Invoice entry | Medium | Financial data, but correctable |
| Client billing | High | Direct financial impact on client |
| Tax filing | Restricted | Regulatory, irreversible, penalties |Don't sell what the agent does on Day 1. Sell where the client will be after 6 weeks of compounding agent learning.
Day 1: The agent knows nothing about the client. It follows templates. It asks for approval on everything. It's slower than doing it yourself.
Week 2: The agent knows the client's preferences. It suggests before being asked. Approval rate is 80%+ on first try. It catches things humans miss.
Week 4: The agent handles routine tasks autonomously. It only escalates edge cases. The client forgot what it was like to do those tasks manually.
Week 6: The agent has built a memory of the business. It anticipates seasonal patterns. It cross-references data across systems. It's doing things the client never thought to automate because it sees patterns they can't.
Use this table in client conversations:
| Dimension | Day 1 | Week 6 |
|---|---|---|
| Knowledge | Template only | Deep client-specific memory |
| Speed | Slower than manual | 10-100x faster than manual |
| Accuracy | 80% (needs review) | 95%+ (exceeds human) |
| Autonomy | Everything needs approval | Routine tasks run independently |
| Scope | 1-2 narrow tasks | Expanding to adjacent workflows |
| Value | "Interesting experiment" | "Can't imagine going back" |
"I want to be honest with you — on Day 1, this agent is going to feel like a new employee who needs training. It'll be slower and it'll ask a lot of questions. That's normal. But unlike a human employee, this agent never forgets what it learns, it works 24/7, and every week it gets faster and more accurate. By Week 6, most clients tell us they can't imagine going back. That's what we're building toward."
Clients often ask: "Why not just use the most powerful AI for everything?"
Different tasks need different levels of AI capability:
Task Complexity → Model Tier
─────────────────────────────────────
Data lookups, formatting → Fast/cheap model (Haiku-class)
Email drafting, summaries → Mid-tier model (Sonnet-class)
Strategic analysis, complex reasoning → Top-tier model (Opus-class)"Think of it like staffing. You wouldn't hire a senior partner to file paperwork, and you wouldn't ask an intern to negotiate a contract. We use the right level of AI for each task — fast and cheap for routine work, powerful and thoughtful for complex decisions. This keeps your API costs manageable while making sure the important stuff gets the best thinking."
Without staggering: All tasks use Opus → ~$X/month API costs
With staggering: 80% Haiku, 15% Sonnet, 5% Opus → ~$X/5 month API costs
Same quality for complex tasks, 80% cost reduction overallPre-deployment:
□ Round 1: Pain Points (Day -14)
□ Round 2: Workflow Mapping (Day -10)
□ Round 3: Constraints (Day -7)
□ Round 4: Solution Design (Day -5)
□ Agreement signed, hardware sourced (Day -3)
Deployment:
□ Layer 1-4 deployment (Day 0-1)
□ Layer 5: Day-1 onboarding (Day 2)
Post-deployment:
□ Daily check-in (Week 1)
□ Tier promotion reviews (Day 3, Week 2)
□ Twice-weekly check-in (Weeks 2-4)
□ Week-6 review and expansion planning© 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 1 other file in skills/client-onboarding-agent of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Client Onboarding Agent 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 |
|---|---|---|---|---|---|---|
| Client Onboarding Agent this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.5k | Automated safety check: Pass | MIT | |
| Money Opsiamzifei/show-me-the-money | 1k | — | ~3.8k | Automated safety check: Pass | Custom licence | |
| Aspiremanagedcode/dotnet-skills | 486 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Deployment Sopbybren-llc/safe-agentic-workflow | 423 | — | ~966 | Automated safety check: Notes | MIT | |
| Deployment SOP Checklistbybren-llc/safe-agentic-workflow | 423 | — | ~950 | Automated safety check: Pass | MIT | |
| Deployment Rollback InterviewerPrepLabsAI/InterviewMentor | 112 | — | ~3.1k | Automated safety check: Pass | MIT |
iamzifei/show-me-the-money
24/7 autonomous business operations orchestrator with business health scoring, canary monitoring, and safety guardrails.
managedcode/dotnet-skills
Build, upgrade, and operate Aspire 13.5.x C or TypeScript application hosts with the current CLI, AppHost, ServiceDefaults, integrations, dashboard, testing, MCP, and deployment patterns for…
bybren-llc/safe-agentic-workflow
Deployment workflows, pre-deploy validation, and smoke testing patterns.
bybren-llc/safe-agentic-workflow
Deployment workflows, pre-deploy validation, smoke testing, and rollback procedures. Use when deploying to staging or production, running smoke tests…
PrepLabsAI/InterviewMentor
A release engineer interviewer managing a failed deployment with spiking error rates.
magnus919/agent-skills
A skill your agent uses when building or operating internal developer platforms: infrastructure as code, CI/CD, container orchestration, service networking, secrets, and observability, or when…
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.
Client onboarding and business diagnostic framework for AI agent deployments. Client Onboarding Agent is an agent skill from LeoYeAI/openclaw-master-skills. Client onboarding and business diagnostic framework for AI agent deployments.
Client Onboarding Agent fits situations like: onboarding new clients for agent deployments; managed automation services.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill client-onboarding-agent -a claude-code`. Or copy the skill folder (skills/client-onboarding-agent in LeoYeAI/openclaw-master-skills) into .claude/skills/client-onboarding-agent in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill client-onboarding-agent -a codex`. Or copy the skill folder (skills/client-onboarding-agent in LeoYeAI/openclaw-master-skills) into .agents/skills/client-onboarding-agent 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 client-onboarding-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/client-onboarding-agent, .gemini/skills/client-onboarding-agent, .github/skills/client-onboarding-agent and .opencode/skills/client-onboarding-agent in your project.
SKILL.md names no scripts, command-line tools or credentials: Client Onboarding Agent is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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. Review the folder before installing.
Client Onboarding Agent is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.5k tokens (SKILL.md is roughly 18k 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 Client Onboarding Agent: Money Ops (iamzifei/show-me-the-money, 1k stars), Aspire (managedcode/dotnet-skills, 486 stars), Deployment Sop (bybren-llc/safe-agentic-workflow, 423 stars) and Deployment SOP Checklist (bybren-llc/safe-agentic-workflow, 423 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 2,160 GitHub stars. The repository holds 1,235 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.