LLM Integration

Kairos uses rig-core to communicate with Claude AI for three core functions: skill extraction, JD analysis, and resume tailoring.

Architecture

graph TB
    subgraph "kairos-core (traits)"
        T0[SkillExtractorService]
        T1[JdAnalysisService]
        T2[ResumeTailorService]
    end

    subgraph "kairos-llm (implementations)"
        SE[ClaudeSkillExtractor]
        A[ClaudeAnalyzer]
        T[ClaudeTailor]
        P0[Skill Extraction Prompts]
        P1[JD Analysis Prompts]
        P2[Resume Tailoring Prompts]
        RC[rig-core Client]
    end

    SE --> T0
    A --> T1
    T --> T2
    SE --> P0
    A --> P1
    T --> P2
    SE --> RC
    A --> RC
    T --> RC
    RC --> API[Claude API]

Skill Extraction

Runs once during resume import to build the user’s Skill Profile.

Prompt Strategy

Temperature: 0.0 (deterministic extraction)

The prompt instructs Claude to:

1. Analyze each work experience and project section from the resume
2. Extract every technical skill mentioned (languages, frameworks, databases, tools, concepts)
3. Estimate proficiency (1-10) based on context clues: duration, depth of usage, role seniority
4. Estimate years of experience per skill
5. Cite evidence — which project/role demonstrates each skill

Output is parsed as a Vec<SkillEntry> via rig-core’s structured output. Users can review and adjust proficiency ratings after generation.

JD Analysis

Prompt Strategy

Temperature: 0.0 (deterministic extraction)

The prompt instructs Claude to:

1. Extract structured data from the JD text
2. Identify required vs preferred skills with estimated proficiency level needed
3. Extract ATS keywords
4. Compare against the user’s Skill Profile (proficiency-aware, not just binary)
5. Compute a match score (0.0 - 1.0) with per-skill breakdown and reasoning

Output is parsed as JSON matching JdAnalysis struct via rig-core’s structured output.

Resume Tailoring

Anti-Hallucination Rules (enforced in system prompt)

1. Only rephrase, reorder, or emphasize existing content
2. Never invent skills, experiences, achievements, or metrics
3. Never change company names, dates, job titles, education details
4. Only modify sections marked as tailorable
5. Preserve the original format (LaTeX or Markdown)

Temperature: 0.3 (controlled creativity)

Validation

After LLM response, a validation step checks:

• No new company names or job titles appeared
• No new degree or institution names
• Dates haven’t changed
• Section types match what was sent

If validation fails, retry with stricter prompt or flag to user.

Output Flow

flowchart TD
    Input["Base Resume Sections\n+ JD Analysis"] --> LLM[Claude via rig-core]
    LLM --> Raw[Raw LLM Response]
    Raw --> Parse[Parse modified sections]
    Parse --> Validate[Validate: no fabricated content]
    Validate --> |Pass| Diff[Generate diff]
    Validate --> |Fail| Retry[Retry with stricter prompt]
    Diff --> User[Show to user for approval]

Cost

Model Configuration

Default: claude-sonnet-4

[api]
claude_model = "claude-sonnet-4"

rig-core’s multi-provider support allows switching to OpenAI or Gemini in the future.