📝 Resume / CV Assistant

AI-powered clawbot skill for resume & CV polishing, job customization, multi-format export, and professional scoring. Version: 1.0.0 · License: MIT · Repository: github.com/Wscats/resume-assistant

Overview

Resume / CV Assistant is a clawbot skill that helps job seekers create, refine, and optimize their resumes and CVs, while adding comprehensive checklist review, scoring, and multi-format export that neither project offers alone.

Usage in AI Agent

Quick Start

Resume / CV Assistant is a standard clawbot skill that can be loaded and invoked by any compatible AI Agent. Here are different integration approaches.

💬 Natural Language (Recommended)

You don't need to memorize any commands — simply describe what you need:

💬 "Create a resume for a software engineer position"
💬 "Polish my resume and fix any issues"
💬 "Optimize my resume for ATS"
💬 "Tailor my resume for this job description: [paste JD]"
💬 "Convert my resume to PDF"
💬 "Score my resume and tell me how to improve"
💬 "What's wrong with my resume?"
💬 "Here's my resume, can you help?"

The assistant understands your intent and automatically routes to the right workflow:

You say	Assistant does
"Create a resume for [role]"	Asks for your background → builds a tailored resume
"Polish / Fix / Improve my resume"	Runs 40+ checklist review → returns polished version
"Optimize for ATS"	Checks ATS compatibility → optimizes keywords & format
"Tailor for this JD: ..."	Analyzes JD → gap analysis → customized resume
"Convert to PDF / Word / ..."	Exports to chosen format with professional template
"Score / Rate / Evaluate my resume"	100-point scoring → strengths & improvement plan
"Here's my resume, help?"	Scores first → suggests next steps

Example Conversations

Creating a new resume:

You:   Create a resume for a frontend engineer position at a startup
Bot:   I'd be happy to help! To get started, could you share:
1. Your work experience (companies, roles, dates, key achievements)
2. Education background
3. Technical skills
4. Any specific job posting you're targeting? (optional)
You:   I have 3 years at Shopify working on React...
Bot:   Here's your tailored resume:
[generates complete resume]
   Would you like me to score, polish, or export it?

Quick improvement:

You:   Here's my resume, what do you think?
       [pastes resume]
Bot:   📊 Resume Score: 68/100 (Grade: C)
Top 3 Issues:
1. ❌ No quantified achievements
2. ⚠️ Weak action verbs
3. ⚠️ Missing keywords for target role
   Would you like me to polish it now?

You:   Yes, polish it

Bot:   [runs full polish with 40+ checklist items]

Job-specific tailoring:

You:   Tailor my resume for this job description:
       Senior Backend Engineer at Stripe
       Requirements: Go, distributed systems, payment APIs...

Bot:   🎯 Job Analysis Complete
📊 Current Match: 62% → After Optimization: 89%
[generates tailored version]

Option 1: Slash Commands via clawbot

For more precise control, use slash commands directly in a clawbot conversation:

/resume polish
Please polish my resume:

John Doe
Senior Frontend Engineer | 5 years experience
Skills: JavaScript, React, Vue, Node.js
...

Option 2: Integration in AI Agent Frameworks

1. Register the Skill

{
  "skills": [
    {
      "name": "resume-assistant",
      "path": "./skills/resume-assistant",
      "manifest": "skill.json"
    }
  ]
}

2. Load Prompts

When handling resume-related requests, prompt files are loaded in this order:

1. prompts/system.md      ← Persona & quality standards (loaded first)
2. prompts/<command>.md    ← Load per command: specific instructions
3. templates/<style>.md    ← Load on demand (export command only)

3. Build the Complete Prompt

Example for

/resume polish

— here's how an AI Agent should construct the prompt:

# Python pseudocode
ROLE_SYS = "system"    # LLM message role constant
ROLE_USR = "user"      # LLM message role constant
def build_prompt(command, args):
# Step 1: Load the skill persona prompt
persona_prompt = load_file("prompts/system.md")
# Step 2: Load command-specific prompt
command_prompt = load_file(f&quot;prompts/{command}.md&quot;)

# Step 3: Combine prompts into LLM messages
combined = persona_prompt + &quot;\n\n&quot; + command_prompt
messages = [
    {&quot;role&quot;: ROLE_SYS, &quot;content&quot;: combined},
    {&quot;role&quot;: ROLE_USR, &quot;content&quot;: args[&quot;resume_content&quot;]}
]

# Step 4: Add optional parameters to user message
if args.get(&quot;language&quot;):
    messages[1][&quot;content&quot;] += f&quot;\n\nLanguage: {args[&#x27;language&#x27;]}&quot;

return messages

// JavaScript pseudocode
const ROLE_SYS = 'system';  // LLM message role constant
const ROLE_USR = 'user';    // LLM message role constant

async function buildPrompt(command, args) {
  // Step 1: Load the skill persona prompt
  const personaPrompt = await loadFile('prompts/system.md');

  // Step 2: Load command-specific prompt
  const commandPrompt = await loadFile(`prompts/${command}.md`);

  // Step 3: Combine prompts into LLM messages
  const combined = `${personaPrompt}\n\n${commandPrompt}`;
  const messages = [
    { role: ROLE_SYS, content: combined },
    { role: ROLE_USR, content: args.resume_content }
  ];

  // Step 4: Add optional parameters
  if (args.language) {
    messages[1].content += `\n\nLanguage: ${args.language}`;
  }

  return messages;
}

Option 3: REST API

If your AI Agent exposes an HTTP API, invoke via RESTful endpoints:

# Polish a resume
curl -X POST https://your-agent-api.com/skills/resume-assistant/polish \
  -H "Content-Type: application/json" \
  -d '{
    "resume_content": "Your resume content...",
    "language": "en"
  }'
Score a resume
curl -X POST https://your-agent-api.com/skills/resume-assistant/score 

-H "Content-Type: application/json" 

-d '{
"resume_content": "Your resume content...",
"target_role": "Senior Frontend Engineer",
"language": "en"
}'
Customize for a job
curl -X POST https://your-agent-api.com/skills/resume-assistant/customize 

-H "Content-Type: application/json" 

-d '{
"resume_content": "Your resume content...",
"job_description": "Job description...",
"language": "en"
}'
Export to a format

curl -X POST https://your-agent-api.com/skills/resume-assistant/export 

-H "Content-Type: application/json" 

-d '{
"resume_content": "Your resume content...",
"format": "html",
"template": "modern"
}'

Option 4: LangChain / LlamaIndex Integration

from langchain.tools import Tool
Define tools based on skill.json commands
resume_tools = [
Tool(
name="resume_polish",
description="Polish and improve resume with 40+ checklist items",
func=lambda input: agent.run_skill(
"resume-assistant", "polish",
{"resume_content": input, "language": "en"}
)
),
Tool(
name="resume_score",
description="Score a resume on 100-point scale with improvement suggestions",
func=lambda input: agent.run_skill(
"resume-assistant", "score",
{"resume_content": input, "language": "en"}
)
),
Tool(
name="resume_customize",
description="Customize resume for a specific job position",
func=lambda input: agent.run_skill(
"resume-assistant", "customize",
{"resume_content": input.split("---JD---")[0],
"job_description": input.split("---JD---")[1],
"language": "en"}
)
),
Tool(
name="resume_export",
description="Export resume to Word/Markdown/HTML/LaTeX/PDF",
func=lambda input: agent.run_skill(
"resume-assistant", "export",
{"resume_content": input, "format": "html", "template": "modern"}
)
),
]

Command Routing

The AI Agent should route user requests to the correct command:

graph TD
    A["User Input"] --> B{"Contains<br/>slash command?"}
    B -- "Yes" --> C{"Parse command"}
    C -- "/resume polish" --> D["Load polish.md"]
    C -- "/resume customize" --> E["Load customize.md"]
    C -- "/resume export" --> F["Load export.md"]
    C -- "/resume score" --> G["Load score.md"]
    B -- "No" --> H{"Intent detection"}
    H -- "polish/improve/fix" --> D
    H -- "job/apply/match" --> E
    H -- "export/download/convert" --> F
    H -- "score/rate/evaluate" --> G
    D --> I["Build Prompt<br/>Call LLM"]
    E --> I
    F --> I
    G --> I
    I --> J["Return Result"]

Argument Validation

The AI Agent should validate arguments before invocation, referencing

skill.json

def validate_args(command, args):
    """Validate arguments against skill.json schema."""
    schema = load_skill_json()
    cmd_schema = next(c for c in schema["commands"] if c["name"] == command)
for arg in cmd_schema[&quot;arguments&quot;]:
    # Check required fields
    if arg[&quot;required&quot;] and arg[&quot;name&quot;] not in args:
        raise ValueError(f&quot;Missing required argument: {arg[&#x27;name&#x27;]}&quot;)

    # Check enum constraints
    if &quot;enum&quot; in arg and arg[&quot;name&quot;] in args:
        if args[arg[&quot;name&quot;]] not in arg[&quot;enum&quot;]:
            raise ValueError(
                f&quot;Invalid value for {arg[&#x27;name&#x27;]}: {args[arg[&#x27;name&#x27;]]}. &quot;
                f&quot;Must be one of: {arg[&#x27;enum&#x27;]}&quot;
            )

    # Apply defaults
    if arg[&quot;name&quot;] not in args and &quot;default&quot; in arg:
        args[arg[&quot;name&quot;]] = arg[&quot;default&quot;]

# Check max resume length
max_len = schema[&quot;config&quot;][&quot;max_resume_length&quot;]
if len(args.get(&quot;resume_content&quot;, &quot;&quot;)) &gt; max_len:
    raise ValueError(f&quot;Resume exceeds {max_len} character limit&quot;)

return args

Commands

/resume polish

Run a 40+ item checklist across 8 categories and get a fully improved resume.

Arguments:

Name Type Required Default Description

resume_content

string

✅

—

Resume text (plain text or Markdown)

language

string

—

en

en

for English,

zh

for Chinese

What you get:

✅/❌/⚠️ checklist results for every item (contact, summary, experience, education, skills, grammar, formatting, ATS)
Fully polished resume with strong action verbs and quantified results
Change summary categorized by priority: 🔴 Critical → 🟡 Major → 🟢 Minor → 💡 Suggestion
Action verb reference table and quantification guide

/resume customize

Tailor your resume for a specific job posting with gap analysis and keyword optimization.

Arguments:

Name Type Required Default Description

resume_content

string

✅

—

Resume text

job_description

string

✅

—

Target job description or job title

language

string

—

en

en

for English,

zh

for Chinese

What you get:

Job description breakdown (required skills, preferred skills, responsibilities, keywords)
Gap analysis matrix mapping every requirement to your resume
Customized resume with keywords naturally integrated
Keyword coverage report: before vs. after
Bonus: cover letter talking points + interview prep notes

/resume export

Convert your resume to Word, Markdown, HTML, LaTeX, or PDF with professional templates.

Arguments:

Name Type Required Default Description

resume_content

string

✅

—

Resume text (Markdown preferred)

format

string

✅

—

word

markdown

html

latex

pdf

template

string

—

professional

professional

modern

minimal

academic

Templates:

Template	Style	Best For
`professional`	Navy, serif headings, classic borders	Finance, consulting, law, healthcare
`modern`	Teal accents, creative layout, emoji icons	Tech, startups, product, marketing
`minimal`	Monochrome, ultra-clean, content-dense	Senior professionals, engineering
`academic`	Formal serif, multi-page, publications	Faculty, research, PhD applications

Export details:

HTML: Self-contained file with embedded CSS, 4 color themes,
```
@media print
```
optimized
LaTeX: Complete compilable
```
.tex
```
with XeLaTeX + CJK support
Word: Pandoc-optimized Markdown with YAML front matter + conversion command
PDF: Print-optimized HTML with A4 page dimensions + multiple conversion methods
Markdown: Clean, structured, version-control friendly

/resume score

Get a 100-point professional evaluation with specific improvement suggestions.

Arguments:

Name Type Required Default Description

resume_content

string

✅

—

Resume text

target_role

string

—

Target role for fit assessment

language

string

—

en

en

for English,

zh

for Chinese

Scoring dimensions (100 points):

Dimension	Points	Evaluates
Content Quality	30	Achievements, action verbs, relevance, completeness
Structure & Formatting	25	Layout, consistency, length, section order
Language & Grammar	20	Grammar, spelling, tone, clarity
ATS Optimization	15	Keywords, standard headings, format compatibility
Impact & Impression	10	6-second test, career story, professionalism

Grade scale: A+ (95-100) → A (90-94) → B+ (85-89) → B (80-84) → C+ (75-79) → C (70-74) → D (60-69) → F (<60)

What you get:

Score breakdown with per-dimension justification
Top 3 strengths with specific examples from your resume
Priority-ranked improvements with Before → After rewrites
Role fit assessment (if target_role provided): fit score, competitive percentile, strengths, gaps
5-step action plan with effort estimates

Recommended Workflow

┌──────────────────────────────────────────────────────┐
│                  Recommended Workflow                 │
├──────────────────────────────────────────────────────┤
│                                                      │
│  1. /resume score     ← Know where you stand         │
│     💬 "Score my resume"                             │
│          │                                           │
│          ▼                                           │
│  2. /resume polish    ← Fix all issues               │
│     💬 "Polish my resume"                            │
│          │                                           │
│          ▼                                           │
│  3. /resume customize ← Tailor per application       │
│     💬 "Tailor for this JD: ..."                     │
│          │                                           │
│          ▼                                           │
│  4. /resume export    ← Generate final files         │
│     💬 "Convert to PDF"                              │
│          │                                           │
│          ▼                                           │
│  5. /resume score     ← Verify improvement           │
│     💬 "Score my resume again"                       │
│                                                      │
└──────────────────────────────────────────────────────┘

Tips:

Start with score if you have an existing resume — understand your baseline
Polish to fix all fundamentals before customizing for a job
Customize separately for each application — one-size-fits-all doesn't work
Export last — get content perfect, then format
Use Markdown as your working format — it converts cleanly to all others
Score again after polish + customize to measure improvement

Project Structure

resume-assistant/
├── skill.json                    # Skill manifest (JSON)
├── skill.yaml                    # Skill manifest (YAML)
├── SKILL.md                      # This documentation
├── prompts/
│   ├── system.md                 # Persona definition & quality standards
│   ├── polish.md                 # Polish prompt: 40+ item checklist
│   ├── customize.md              # Customize prompt: gap analysis & keywords
│   ├── export.md                 # Export prompt: 5 formats × 4 templates
│   └── score.md                  # Score prompt: 100-point rubric
├── templates/
│   ├── professional.md           # Classic corporate template
│   ├── modern.md                 # Contemporary tech/startup template
│   ├── minimal.md                # Ultra-clean senior template
│   ├── academic.md               # Formal academic CV template
│   └── export/
│       ├── resume.html           # HTML template (4 CSS themes)
│       └── resume.tex            # LaTeX template (XeLaTeX + CJK)
└── examples/
    ├── sample-resume-en.md       # English sample (high quality)
    ├── sample-resume-zh.md       # Chinese sample (high quality)
    ├── sample-resume-weak.md     # Weak sample (for scoring demo)
    └── usage.md                  # Usage examples & workflow guide

Language Support

Language	Code	Features
English	`en`	Full support, US/UK conventions
Chinese	`zh`	Full support, 中英文混排规范, CJK export

Configuration

Key	Value	Description
`max_resume_length`	10,000 chars	Maximum input length
`supported_languages`	`en` , `zh`	Available languages
`supported_export_formats`	word, markdown, html, latex, pdf	Available export formats
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📝 Resume / CV Assistant

Overview

Usage in AI Agent

Quick Start

💬 Natural Language (Recommended)

Example Conversations

Option 1: Slash Commands via clawbot

Option 2: Integration in AI Agent Frameworks

1. Register the Skill

2. Load Prompts

3. Build the Complete Prompt

Option 3: REST API

Score a resume

Customize for a job

Export to a format

Option 4: LangChain / LlamaIndex Integration

Define tools based on skill.json commands

Command Routing

Argument Validation

Commands

/resume polish

/resume customize

/resume export

/resume score

Recommended Workflow

Project Structure

Language Support

Configuration

Quick Start

Manual Installation

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`/resume polish`

`/resume customize`

`/resume export`

`/resume score`