sunanzhe2004/shushu-internship-resume-optimizer/shushu-internship-tool

Shushu Internship Tool

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Goal

Turn messy internship materials into:

• resume-ready achievements
• JD-oriented resume bullets
• evidence-backed project summaries
• business-context explanations
• interview-ready STAR and Q&A material

Core Principle

Prefer skill-level reusable methodology over user-specific hard scripts.

This means:

• use model understanding first for project grouping, contribution extraction, value judgment, and title generation
• use scripts for stable work only: file loading, normalization, schema validation, deduplication, ranking, formatting, and output writing
• treat fixture materials as regression checks, not as the source of domain rules
• avoid growing keyword-to-title maps as the main extraction strategy
• if a rule is added, it should express a broad pattern such as structure, causality, evidence quality, or value filtering

Preferred Workflow

1. Gather Inputs

Prefer collecting:

• target JD
• target role direction
• current internship scope
• available materials: code_repo / project_summary / business_docs / PR / notes / retrospectives
• known metrics or business outcomes
• confidentiality constraints

2. Normalize Into sources.json

Supported source types:

• code_repo
• project_summary
• business_docs

When possible, prefer adding model-structured extraction to textual sources via:

• structured_extract_path
• structured_extract

This is preferred over forcing long free-form notes through brittle fallback parsing.

3. Achievement Audit

Run:

python -m shushu_internship_tool.achievement_audit --sources sources.json --out reports/audit

Focus on:

• whether project blocks are split correctly
• whether each achievement has real business context
• whether value comes from outcomes rather than workload counts
• whether user-check flags expose weak claims, AI-ish wording, or missing evidence

4. Resume Ranking

Run:

python -m shushu_internship_tool.resume_rank --jd jd.txt --achievements reports/audit/achievement_audit.json --target-role backend --out reports/rank

Focus on:

• which achievements are worth keeping for the target role
• which bullets should stay separate vs. merge
• whether bullet ordering follows causal flow: setup -> mechanism -> result
• choose bullet density by project count instead of defaulting to a fixed 3 to 4 bullets
• if the user says the project is only one part of their work, merge same-mainline bullets first; but if the material is one complete project, 4 to 5 bullets can still be reasonable
• review the final resume summary for scanability before presenting it: repeated openings, overlong bullets, and over-splitting should be fixed first
• scripts may suggest merge candidates, but only the skill / prompt layer should decide whether two bullets or modules actually belong in one resume line

5. Interview Pack

Run:

python -m shushu_internship_tool.interview_pack --project-notes reports/rank/resume_rank.json --target-role backend --out reports/interview

Focus on:

• whether the one-minute intro sounds spoken rather than templated
• whether STAR is traceable back to evidence
• whether business follow-up questions can be answered cleanly

Model-First Extraction Guidance

For semantic tasks, prefer model output in structured form.

Recommended model responsibilities:

• split raw materials into projects
• decide whether two modules belong to one project
• extract background / task / actions / outcome / business_value
• identify whether a bullet is worth keeping
• suggest merge candidates
• generate merged wording only after the prompt layer has decided that a merge is actually appropriate
• rewrite titles into concise, resume-usable wording
• make the final semantic judgment on whether similar-looking work should stay split because project boundary, audience, deliverable, or ownership is different

Recommended script responsibilities:

• accept structured extraction
• normalize fields into downstream schema
• attach evidence and warnings
• rank and format outputs
• provide fallback parsing only when structured extraction is absent
• enforce light guardrails such as bullet-count caps, scanability reminders, and readability review
• never hard-code semantic merge decisions that depend on project boundary understanding
• avoid role-track-specific writing templates when a generic structured summary is enough

See:

• model-first-extraction.md

Anti-Patterns

Avoid these unless there is no better option:

• adding project-specific keyword tables derived from one user's materials
• treating one fixture file as the canonical project structure
• using file counts, reading volume, or review volume as achievements without value linkage
• collapsing business background and achievements into a single bullet
• using fallback parsing output as if it were model-quality understanding

Output Style

• optimize for reusable, editable outputs rather than flashy one-shot writing
• never fabricate metrics
• prefer evidence, action, outcome, and business value over buzzwords
• keep uncertainty explicit when evidence is incomplete
• prefer fewer, denser bullets over many thin bullets when several points belong to the same project mainline
• default compression strategy:
• 1 project: 4 to 5 bullets is acceptable when the project is complete and each bullet carries a distinct main point
• 2 projects: 2 to 3 bullets per project is usually reasonable
• 3+ projects: usually 1 to 2 bullets per project in the resume-facing summary
• before returning the final artifact, do a readability review:
• can a recruiter scan it in a few seconds
• are same-project bullets repetitive enough to merge
• is any bullet carrying background, action, and result in a bloated way
• if project boundary is ambiguous, stay conservative in the final artifact: keep modules split first, then let the prompt explain possible merge options