Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Score and rewrite resume bullets to remove AI writing patterns and apply engineering prose. Uses a 7-dimension scoring system — not a hard banned-word filter. Writing style is inferred from the JD Intelligence Briefing, not from company name.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill as Phase 3 — after bullet generation (PACTI), before LaTeX formatting. Run it on all bullets before they enter the LaTeX template.
Trigger phrases: "humanize my resume", "remove AI language", "make this sound more natural", "style check", "review my bullets"
Also use proactively when you detect:
This is a style checker, not a word filter. Words are not banned. They are penalized when overused or when they substitute for specific technical content. "Leveraged Kubernetes autoscaling" is perfectly natural. "Leveraged cutting-edge AI to deliver innovative solutions" is not. The difference is specificity, not the word itself.
Score the full set of resume bullets on each dimension (0–10). All dimensions must meet their target before the resume proceeds to LaTeX generation.
| # | Dimension | Target Score | What's Measured |
|---|---|---|---|
| 1 | AI Buzzword Score | ≤2/10 | Frequency and clustering of AI-cliché words |
| 2 | Specificity Score | ≥8/10 | Every bullet names ≥1 technology, algorithm, system, or proper noun |
| 3 | Evidence Score | 10/10 | Every bullet traceable to the evidence database or GitHub source |
| 4 | Metric Density | ≥5/10 | Measurable outcomes preferred; concrete technical implementation details (named technologies, specific endpoints, component counts) count toward this score when real metrics aren't available |
| 5 | Technical Density | ≥6/10 | Architecture / algorithm / design reasoning present in technical bullets. PACTI "Core Technical Decision" is recommended but not mandatory — direct implementation statements with named technologies also score well |
| 6 | Readability | ≥8/10 | Max 1 adjective per bullet; no filler words; ≤2 lines when rendered |
| 7 | Interview Defensibility | 10/10 | Every bullet has a Q&A pair in the evidence database that the candidate can answer |
These words are not banned. They are penalized if:
| Word | Acceptable Use | Flagged Use |
|---|---|---|
| leveraged | "Leveraged Kubernetes autoscaling to handle traffic spikes" | "Leveraged cutting-edge AI techniques" |
| spearheaded | "Spearheaded migration to microservices (once, specific)" | Used 3× across resume |
| pioneered | "Pioneered adoption of Rust in the team's backend codebase" | "Pioneered innovative solutions" |
| orchestrated | "Orchestrated LangGraph multi-agent pipeline" | "Orchestrated seamless collaboration" |
| championed | Used once, describing specific advocacy | Used as vague leadership claim |
| utilized | Almost always replaceable with "used" | "Utilized various technologies" |
| seamless | Should never appear — always vague | Any usage |
| cutting-edge | Should never appear — always vague | Any usage |
| innovative | Should never appear — claims are hollow | Any usage |
| robust | Should never appear — show it, don't say it | Any usage |
| passionate | Should never appear in technical bullets | Any usage |
| dynamic | Should never appear — always vague | Any usage |
| results-driven | Should never appear — should be evident | Any usage |
| synergy | Should never appear | Any usage |
| state-of-the-art | Should never appear — use specific benchmark | Any usage |
| Instead of | Use |
|---|---|
| leveraged, utilized | built, used, applied |
| spearheaded | led, initiated, designed |
| pioneered | introduced, created, first to implement |
| orchestrated | coordinated, built, structured |
| championed | advocated for, proposed, implemented |
| seamless | (delete — describe what was smooth instead) |
| cutting-edge | name the specific technology |
| innovative | describe what was novel specifically |
Every bullet must contain at least one of:
Specificity check — rewrite triggers:
❌ "Built a system to handle large amounts of data efficiently"
✅ "Built a batch processing pipeline with Apache Kafka consuming 50K events/hour"
❌ "Implemented machine learning for anomaly detection"
✅ "Implemented Isolation Forest anomaly detection on unlabeled infrastructure logs"
❌ "Improved system performance significantly"
✅ "Reduced API response time from 850ms to 210ms by adding Redis caching"
Technical bullets should ideally contain engineering reasoning, not just facts. The PACTI structure helps with this — specifically the "Core Technical Decision" step — but is NOT mandatory for every bullet.
Low technical density (rewrite):
Built an anomaly detection system using Isolation Forest.
High technical density (keep — PACTI style):
Chose Isolation Forest over DBSCAN for anomaly detection because the log data lacked labels; the unsupervised nature of IF made it robust to varying log densities across microservices.
Acceptable technical density (keep — direct style):
Built a REST API with FastAPI serving 4 endpoints for log ingestion and retrieval, with structured error handling and request validation using Pydantic models.
The second example uses PACTI and explains why. The third example doesn't use PACTI but still has high specificity (named technology, component count, specific patterns). Both are acceptable. Not every bullet needs to be a mini architecture decision record.
Delete on sight:
Maximum 1 adjective per bullet. Adjectives that add specificity are fine ("asynchronous", "distributed", "real-time"). Adjectives that are opinions are not ("innovative", "robust", "efficient" without a metric).
Maximum 2 lines when rendered in LaTeX. If a bullet is longer:
The style profile from the JD Intelligence Briefing is applied here. The tone, verb choices, and emphasis shift based on the 2D matrix output.
| Profile | Tone Guidance |
|---|---|
| Startup/OS × Eng Manager | Ownership language: "designed", "built end-to-end", "shipped". Architecture reasoning required. |
| Enterprise × Eng Manager | Reliability and scale: SLA, fault tolerance, distributed system context. |
| Research × Researcher | Methodology and rigor: experiment design, datasets used, evaluation metrics |
| DevTools × Recruiter | Developer experience: API design quality, adoption metrics, documentation |
| Big Tech × Eng Manager | Scale and impact: users served, requests/sec, latency, infra cost |
| Open Source × Recruiter | Contribution clarity: PRs merged, stars, community impact |
Run the 7-dimension scorecard on the full bullet set. Output:
## Humanizer Report
### Scores
| Dimension | Score | Status |
|---|---|---|
| AI Buzzword | 1/10 | ✅ Pass |
| Specificity | 9/10 | ✅ Pass |
| Evidence | 10/10 | ✅ Pass |
| Metric Density | 7/10 | ✅ Pass |
| Technical Density | 8/10 | ✅ Pass |
| Readability | 9/10 | ✅ Pass |
| Interview Defensibility | 10/10 | ✅ Pass |
### Applied Style Profile
[Startup/OS × Eng Manager]
### Flags
- Bullet 3: "utilized various techniques" → No specific technology named. Rewrite required.
- Bullet 7: "spearheaded" used twice across resume. Second usage flagged. Rewrite one.
### Rewrites
**Original:** "Utilized various NLP techniques for text processing"
**Revised:** "Applied spaCy for tokenization and entity extraction on 50K Hinglish support tickets"
**Original (Bullet 7 second use):** "Spearheaded data pipeline improvements"
**Revised:** "Redesigned the ingestion pipeline to process logs asynchronously, reducing backlog from 4 hours to under 10 minutes"
まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Decides whether to apply to a specific role, with what level of effort, and with which resume variant. Prevents wasted applications on roles with fundamentally poor fit and prioritizes high-probability opportunities.
日本語の概要は準備中です。原文の説明を表示しています。
Decide how to prioritize job applications using hard eligibility, evidence coverage, role alignment, and real application outcomes rather than arbitrary fit scores.
日本語の概要は準備中です。原文の説明を表示しています。
Define a clear primary role identity and supporting evidence so a resume reads coherently for the target job.
日本語の概要は準備中です。原文の説明を表示しています。
Determines the candidate's primary role identity before any JD analysis. Classifies into role families, selects core tech identity, ranks projects by role relevance, and outputs a Candidate Position Brief consumed by all downstream stages. This is Phase -1 of the pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Translate skills from one industry to another, identify transferable skills
日本語の概要は準備中です。原文の説明を表示しています。