placed-resume-optimizer（放置简历优化器）

Placed Resume Optimizer

AI-powered resume optimization for ATS compatibility, keyword matching, and bullet point quality — all via the Placed API. No MCP server required.

API Key

Load the key from ~/.config/placed/credentials, falling back to the environment:

if [ -z "$PLACED_API_KEY" ] && [ -f "$HOME/.config/placed/credentials" ]; then
  source "$HOME/.config/placed/credentials"
fi

If PLACED_API_KEY is still not set, ask the user:

"Please provide your Placed API key (get it at https://placed.exidian.tech/settings/api)"

Then save it for future sessions:

mkdir -p "$HOME/.config/placed"
echo "export PLACED_API_KEY=<key_provided_by_user>" > "$HOME/.config/placed/credentials"
export PLACED_API_KEY=<key_provided_by_user>

How to Call the API

placed_call() {
  local tool=$1
  local args=${2:-'{}'}
  curl -s -X POST https://placed.exidian.tech/api/mcp \
    -H "Authorization: Bearer $PLACED_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"tools/call\",\"params\":{\"name\":\"$tool\",\"arguments\":$args}}" \
    | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['result']['content'][0]['text'])"
}

Available Tools

Usage Examples

Check ATS compatibility:

placed_call "check_ats_compatibility" '{"resume_id":"res_abc123"}'
# Returns: ATS score, formatting recommendations

Get quality score:

placed_call "get_resume_quality_score" '{"resume_id":"res_abc123"}'
# Returns: overall score, breakdown by section

Analyze gaps vs. a job description:

placed_call "analyze_resume_gaps" '{
  "resume_id": "res_abc123",
  "job_description": "Senior Software Engineer at Stripe — Go, distributed systems, Kafka..."
}'
# Returns: critical gaps, keyword gaps, suggestions

Score resume-job match:

placed_call "match_job" '{
  "resume_id": "res_abc123",
  "job_description": "..."
}'
# Returns: match score 0-100, matched/missing keywords

Optimize resume for a job:

placed_call "optimize_resume_for_job" '{
  "resume_id": "res_abc123",
  "job_description": "Senior Software Engineer at Airbnb..."
}'
# Returns: suggested section improvements

Improve a single bullet:

placed_call "improve_bullet_point" '{
  "bullet_point": "Worked on database optimization",
  "context": "Senior SRE at Uber"
}'
# Returns: rewritten bullet with metrics and impact

Optimize a specific section:

placed_call "optimize_resume_section" '{
  "resume_id": "res_abc123",
  "section_type": "experience",
  "section_data": "Current bullets here...",
  "job_description": "Target job description..."
}'

Optimization Workflow

Run this before every application:

1. match_job — check current fit score
2. analyze_resume_gaps — identify missing keywords
3. check_ats_compatibility — catch formatting issues
4. improve_bullet_point — strengthen weak bullets
5. optimize_resume_for_job — get full tailoring suggestions
6. match_job again — confirm score improved

Bullet Point Formula

[Action Verb] + [What You Did] + [How/Scale] + [Quantified Result]

Before: "Worked on database optimization" After: "Optimized PostgreSQL query performance by 40%, reducing p99 latency from 500ms to 300ms for 10M+ daily users"

Strong action verbs:

• Technical: Architected, Built, Designed, Optimized, Implemented, Engineered, Migrated
• Leadership: Led, Managed, Mentored, Spearheaded, Directed
• Impact: Improved, Reduced, Increased, Accelerated, Scaled, Transformed

ATS Compatibility Rules

Tips

• ATS score below 70 → fix formatting first, then keywords
• Run match_job before and after optimizing to measure improvement
• Always review optimize_resume_for_job suggestions before applying
• Keep a master resume and create tailored copies per application

Additional Resources

• references/api-guide.md — Full API reference with scoring rubrics and response schemas