shushu-internship-tool

name	shushu-internship-tool
description	Use when an AI assistant helps users turn ongoing internship work into resume-ready and interview-ready material from code repos, project summaries, and business documents.

Shushu Internship Tool

Default output is Chinese, while preserving English technical terms, commands, and repository names.

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