cv-linkedin

name	cv-linkedin
description	Analyze and improve a CV and LinkedIn profile for software industry roles. Use when the user asks to "improve my CV", "review my resume", "optimize my LinkedIn profile", "help with my LinkedIn", "make my resume better", "create a CV", "prepare my resume for job applications", "update my LinkedIn", or anything about career documents for software engineers, developers, product managers, product owners, engineering managers, tech leads, SREs, DevOps engineers, data engineers, ML engineers, or other software-industry roles. The user must provide a CV file path and a path to an unzipped LinkedIn data export directory (obtained via LinkedIn's "Get a copy of your data" feature). If either is missing, ask for both before proceeding and — if the export is missing — walk the user through requesting it.
tools	Read, Write, Glob, TaskCreate, TaskUpdate, SkillInvoke(document-skills:pdf)
model	sonnet

CV & LinkedIn Improver — Software Industry Edition

Analyze the user's CV and LinkedIn profile for a software industry role, then produce an improved CV and a LinkedIn optimization guide. All advice is role-aware: the expectations for a Staff Engineer differ from those for a Senior Product Manager or an Engineering Manager.

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Task Tracking

Before doing any work, call TaskCreate for each phase below. Call TaskUpdate (status in_progress) when you begin a phase and TaskUpdate (status completed) when you finish it.

• Read CV
• Detect role type
• Read LinkedIn data export
• Analyze CV
• Analyze LinkedIn profile
• Write improved CV
• Write LinkedIn improvement guide
• Print summary

---

Phase 0: Gather Inputs

Before doing anything else, confirm you have both of:

1. CV file path — path to their CV (plain text, Markdown, PDF text, or similar).
2. LinkedIn data export directory — path to a directory containing the CSV files from LinkedIn's official "Get a copy of your data" export (at minimum Profile.csv, Positions.csv, Education.csv, Skills.csv). May be a path to an unzipped directory or a path to the .zip itself — if a .zip, unzip it in place first.

Direct LinkedIn fetching is not supported by this skill: LinkedIn's User Agreement prohibits automated extraction, the live page only server-renders the top card (About / Experience / Education / Skills are lazy-loaded behind authenticated XHR calls), and cookie-based scraping risks account restrictions. The data export is LinkedIn's sanctioned alternative and gives complete, structured, source-of-truth data.

If either input is missing, ask for both at once. If the user does not yet have the LinkedIn export, give them this instruction verbatim and wait:

To get your LinkedIn data:

1. Open https://www.linkedin.com/mypreferences/d/download-my-data
2. Choose "Want something in particular?" and tick at least: Profile, Positions, Education, Skills, Certifications, Languages, Projects.
3. Click Request archive, re-enter your password.
4. The fast archive arrives by email in ~10 minutes; download and unzip it.
5. Reply with the path to the unzipped folder (and your CV path).

If the user explicitly says to skip LinkedIn (e.g. "skip LinkedIn", "just the CV"), proceed with only the CV. Skip Phases 2 (Read LinkedIn export), 4 (Analyse LinkedIn), and 6 (Write LinkedIn guide). Note the skip in the final summary.

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Phase 1: Read the CV

Read the file at the path the user provided. If the file is not found, report the error and stop.

PDF files (.pdf extension):

• Log: "CV format: PDF — checking for document-skills:pdf skill."
• If the document-skills:pdf skill is available in your environment, invoke it to extract the CV text and then log: "Reading CV using document-skills:pdf skill."
• If it is not available, use the Read tool directly and log: "Reading CV as PDF using Read tool (document-skills:pdf not installed)."

All other formats (Markdown, plain text, Word, etc.): use the Read tool and log: "Reading CV as [format] using Read tool."

Parse the content into these sections (names vary — match by content, not heading label):

• Contact: name, email, phone, location, GitHub URL, portfolio/personal site, LinkedIn URL
• Summary / Profile / Objective: the opening pitch
• Experience / Work History: each role with title, company, dates, and bullet points
• Education: degrees, institutions, dates; also bootcamps and relevant MOOCs
• Skills / Technical Skills: keyword lists or grouped categories
• Projects: personal or open-source projects (especially relevant for engineers)
• Certifications: cloud certs (AWS, GCP, Azure), Kubernetes (CKA/CKAD), Scrum, PMP, etc.
• Publications / Talks: conference talks, blog posts, papers (senior/staff level)

Note which sections are present, absent, or thin.

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Phase 1b: Detect Software Role Type

Classify the user's target role from their current title, past titles, and skills. This classification drives all downstream advice — do not skip it.

Engineer — any variant of: Software Engineer, Developer, SRE, DevOps Engineer, Data Engineer, ML Engineer, Platform Engineer, Security Engineer, Frontend, Backend, Full-Stack, Mobile, Embedded, QA Engineer. Signals: programming languages in skills, technical architecture bullets, GitHub link, system design language.

Product Manager / Product Owner — any variant of: PM, APM, Senior PM, GPM, Director of Product, VP Product, Product Owner, Product Lead. Signals: roadmap, user research, A/B testing, PRD, OKRs, go-to-market, stakeholder management language.

Engineering Manager / Technical Leader — any variant of: Engineering Manager, EM, Staff/Principal/Distinguished Engineer, Tech Lead, VP Engineering, CTO, Director of Engineering, Head of Engineering. Signals: reports/headcount numbers, hiring, performance reviews, org-level impact, both people management AND technical decision language.

Other software roles (Designer, Data Scientist, Analyst, etc.) — apply general advice; note the role and adapt as best as possible.

If the role cannot be determined from the CV, ask: "What type of role are you targeting? (e.g. Software Engineer, Product Manager, Engineering Manager)"

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Phase 2: Read the LinkedIn Data Export

Read the CSVs from the export directory. See references/linkedin-export-schema.md for the exact column names, the Month YYYY date format, and the multi-line Description field gotcha (use a real CSV parser — do not split on commas yourself).

Required CSVs (skill stops with an error if any are missing):

• Profile.csv — single-row CSV. Extract First Name, Last Name, Headline, Summary (= LinkedIn About), Industry, Geo Location, Websites, Twitter Handles.
• Positions.csv — one row per role. Extract Company Name, Title, Description, Location, Started On, Finished On (empty string = current role).
• Education.csv — one row per entry. Extract School Name, Start Date, End Date, Degree Name, Notes, Activities.
• Skills.csv — one row per skill. Extract Name.

Optional CSVs (use if present, do not fail if absent):

• Certifications.csv — Name, Authority, Started On, Finished On, License Number, Url.
• Languages.csv — Name, Proficiency.
• Projects.csv — Title, Description, Started On, Finished On, Url.
• Volunteering.csv — Company Name, Role, Started On, Finished On, Cause, Description.

Reading rules:

• Use a real CSV parser. The Description and Summary fields commonly contain embedded newlines, commas, and quotes.
• LinkedIn export column names vary slightly across vintages. If a required column is not found by its expected name, look for the close variants documented in references/linkedin-export-schema.md (e.g. Started On vs Start Date).
• If the directory exists but a required CSV is missing or empty, list which file is missing and ask the user to re-export with the right categories ticked. Do not proceed to Phase 4.
• If the path the user provided is a .zip, unzip it in place (to a sibling folder named like the zip without .zip) and use that as the export directory.
• Sort Positions.csv rows by Started On descending so the most recent role is processed first; rows with empty Finished On are current and rank highest.

Note which optional CSVs are absent — this affects Phase 4 (e.g. no Certifications.csv means certifications cannot be audited; do not invent any).

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Phase 3: Analyse the CV

3a. Summary / Profile Section

• Missing? → Flag. Recruiters read the top third first; no summary = no pitch.
• Generic phrases? Flag any of: results-oriented, dynamic, passionate about, team player, hard worker, detail-oriented, fast learner, self-starter, go-getter, ninja, rockstar. These add zero signal.
• First person? "I am a developer..." → rewrite as a statement with no grammatical subject.
• Length? Ideal: 3–5 lines, 40–60 words. Flag if over 80 words.
• Role-specific expectations:
  • Engineer: should mention primary stack or domain (e.g. "backend engineer specialising in distributed systems and Go"), seniority signal (years or scope), and a standout outcome.
  • PM: should mention the type of product (B2B SaaS, consumer, platform, growth) and the scale or impact of past work (users, revenue, team size). "I love building products" is not a pitch.
  • EM / Tech Lead: should name team size or org scope, the type of engineering culture or transformation driven, and career-level signal (managed N engineers, built N teams).

3b. Work Experience Bullets

For each bullet across all roles, check:

Weak verb openers — always flag: helped, worked on, assisted with, was responsible for, involved in, participated in, supported, contributed to, played a role in, tried to

Strong verb banks by role:

Role	Strong Verbs
Engineer	engineered, architected, built, shipped, refactored, migrated, automated, reduced, optimised, scaled, deployed, integrated, designed, implemented, debugged
PM / PO	defined, launched, prioritised, drove, shipped, grew, negotiated, aligned, validated, synthesised, owned, partnered, increased, reduced, delivered
EM / TL	led, hired, grew, scaled, restructured, established, mentored, unblocked, defined, championed, partnered, transformed, delivered, set, cultivated

Outcome check — flag bullets that are pure task, with no outcome of any kind.

A bullet is complete if it carries at least one of:

• A number (latency, throughput, users, cost, dollar amount, percent, team size).
• A scope or scale phrase ("across 12 services", "for a 30-engineer org", "in a 2M-user product").
• A named system or shipped artifact ("the order-orchestration service", "v2 of the billing API", "the fraud-detection pipeline").
• A before/after comparison, even without numbers ("migrated from REST to gRPC", "replaced the bash deploy script with GitLab CI").
• A complexity / problem signal ("designed the leader-election protocol", "debugged a memory leak in the JVM scheduler").

A bullet that has none of those — e.g. "Wrote unit tests", "Worked on backend services", "Participated in code reviews" — is pure task; flag it.

Numbers are one way to add outcome, not the only way. Invented or trivially small numbers are worse than no number — recruiters and hiring managers spot them. For engineers, scope and named-system signals are equally legitimate; for PMs and EMs, hard numbers carry more weight because business KPIs and team metrics are inherent to the role.

Role	Metric types when a number IS the right outcome
Engineer	Latency (ms), throughput (req/s), uptime (%), test coverage (%), build/deploy time, lines of code removed, cost savings ($)
PM / PO	MAU/DAU, conversion rate (%), ARR/MRR ($), NPS, sprint velocity, time-to-market, number of experiments run
EM / TL	Team size (#), hiring targets met (#), attrition rate (%), delivery on-time (%), promotion rate, number of teams/squads managed

A pure-task bullet needs [OUTCOME: e.g. <scope phrase> OR <named system> OR <number>] appended — listing at least two non-number options so the user is not pushed to invent a metric.

Result vs. task: "Wrote unit tests" (task) vs. "Achieved 94% test coverage on the payments service, catching 3 critical regressions before they reached production" (number-as-outcome) vs. "Built the regression-test harness for the payments service" (named-system-as-outcome) — both rewrites are valid.

3c. Education and Certifications

• Degrees fully named? "CS degree" → "B.Sc. Computer Science, University of X, Year"
• Graduation year present?
• Certifications (software-specific): Flag if the person has cloud/infra experience but no cloud cert listed, or does agile/scrum work but no cert — worth adding if held. Certs that matter: AWS SA/Developer/SysOps, GCP ACE/PCA, Azure AZ-900/AZ-204, CKA/CKAD, CSPO, CSM, PMP.

3d. Skills Section

• Present? Missing entirely is a serious ATS problem.
• Structure by role:
  • Engineer — group by: Languages, Frameworks & Libraries, Databases, Cloud & Infrastructure, Testing, Tools. Flat dumps are hard to skim.
  • PM — group by: Product Methodology, Analytics & Research, Tools, and optionally Technical Background if relevant.
  • EM / TL — group by: Leadership & Management, Engineering Practices, Technical Background, Tools.
• Technologies mentioned in experience bullets but absent from the skills section?
• Consistent casing: JavaScript (not Javascript), TypeScript, PostgreSQL, AWS, Kubernetes, GraphQL, CI/CD.

3e. Software-Specific Signals

• GitHub / portfolio link in contact section?
  • Engineers: flag if absent — recruiters and technical interviewers look for this. Even an empty profile is better than none; note the gap.
  • PMs / EMs: optional but valuable if they have public writing or side projects.
• Open-source contributions listed? For engineers, these are strong differentiators.
• Projects section present for engineers with fewer than 5 years experience? Essential if the work history is thin.
• Tenure signals: flag any unexplained gap longer than 6 months.
• ATS keyword coverage: verify that key terms from the user's target role appear in the CV. Common ATS terms by role:
  • Engineer: the actual technology names (React, Kubernetes, Python, etc.) must be spelled out in full — acronyms alone fail ATS parsers.
  • PM: "product roadmap", "A/B testing", "user research", "cross-functional", "go-to-market", "OKRs", "KPIs".
  • EM: "engineering management", "agile", "performance management", "technical strategy", "stakeholder management".

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Phase 4: Analyse the LinkedIn Profile

(Skip entirely if the user chose to skip LinkedIn in Phase 0.)

The data here is parsed from CSVs in Phase 2, so every check below operates on concrete values — do not hedge with "if the headline is present". A field is either present (non-empty string in the CSV) or absent (empty string / missing column). Treat absent as a flagged finding, not a question to ask the user.

4a. Headline

If it is only the job title + company, it wastes the highest-visibility field on LinkedIn. LinkedIn headline is the #1 field recruiters use in search filters.

Headline templates by role:

Role	Template
Engineer	{Seniority} {Specialisation} Engineer · {Primary Stack} · {Domain or Impact}
PM	{Seniority} Product Manager · {Product Type, e.g. B2B SaaS / Consumer / Platform} · {Impact angle, e.g. 0-to-1 / Growth / Enterprise}
EM / TL	Engineering Manager · {Team/Org context} · {Scale or domain, e.g. 40-person org / Fintech / Developer Tooling}

Examples:

• "Senior Backend Engineer · Go & Kafka · Building high-throughput fintech systems"
• "Product Manager · B2B SaaS · 0-to-1 Products & Enterprise Expansion"
• "Engineering Manager · 3 Teams, 24 Engineers · Platform & Infrastructure"

4b. About / Summary

• Present? (LinkedIn penalises completeness score if absent; profile ranks lower in search.)
• Hook? First 2 visible lines (before "...see more") must earn the click.
• Flow: what you do → what you are known for / strongest credential → what you are looking for → contact CTA.
• Role-specific must-haves:
  • Engineer: mention primary language/stack and one scale or performance achievement.
  • PM: mention product type and one business or user impact metric.
  • EM: mention team size and one org or delivery achievement.

4c. Experience Descriptions

• Roles with no descriptions are a missed opportunity — LinkedIn allows far more than a CV.
• Descriptions should expand on the CV, not copy-paste it.
• Engineers: describe the technical problem and the solution, not just the outcome.
• PMs: describe the business context (what problem, who the users were, what you shipped).
• EMs: describe the team's mission and your contribution to org health and delivery.

4d. Skills Section

• Top 3 featured skills should be the most recruiter-relevant for the target role.
• Engineer: feature the primary programming language, the main framework/platform, and either the cloud provider or a relevant specialisation.
• PM: feature "Product Management", the domain (e.g. "SaaS", "B2B", "Mobile"), and a methodology or tool (e.g. "Product Strategy", "A/B Testing").
• EM: feature "Engineering Management", a delivery methodology, and either a technical domain or a scale signal (e.g. "Team Building", "Agile", "Distributed Systems").

4e. CV vs. LinkedIn Alignment

For each Positions.csv row, find the matching CV role by Company Name. Compare:

• Title vs. CV title — flag any difference (recruiters notice and ask).
• Started On / Finished On vs. CV dates — flag a mismatch greater than ~1 month. The CSV format is Month YYYY; CV dates may be YYYY only or a range — normalise to year before comparing.
• Roles present on the CV but absent from Positions.csv (LinkedIn looks abandoned for that period).
• Roles present in Positions.csv but absent from the CV (CV looks incomplete or there's a deliberate omission worth surfacing).

For Education, do the same comparison using School Name as the join key.

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Phase 5: Write Improved CV

Write the improved CV to cv-improved.md in the same directory as the original CV file. If the CV content was pasted directly (no file path), write to ./cv-improved.md.

Rules:

• Rewrite the summary using a role-specific template (use content already in the CV):
  • Engineer: {Seniority} {specialisation} engineer with {X} years building {domain} systems. Known for {technical strength from bullets}. Track record of {performance or scale achievement}.
  • PM: Product manager with {X} years shipping {product type} for {user type}. Known for {strength, e.g. "0-to-1 launches" / "growth experimentation"}. Track record of {business outcome type}.
  • EM: Engineering manager with {X} years leading {N}-person teams in {domain}. Known for {strength, e.g. "building high-trust teams" / "technical transformation"}. Track record of {delivery or org outcome}.
• Replace every weak verb with a strong one from the role-specific verb bank in Phase 3b.
• For every pure-task bullet (no outcome of any kind, per the Phase 3b outcome check), append [OUTCOME: e.g. <scope phrase>, <named system>, or <number>], listing at least two non-number suggestions drawn from the role and surrounding context. Do not append this to bullets that already carry scope, a named system, or a before/after comparison — even if no digit is present; those are already complete.
• Do not invent facts. Preserve company names, dates, titles, and technologies exactly.
• If the Skills section was absent, create one using the role-appropriate category structure from Phase 3d, populated from technologies named in experience bullets.
• For engineers: if no GitHub link is in the contact section, add a placeholder GitHub: github.com/[your-username] with a note that they should add the real link.
• Format: clean Markdown. ## for section headers, ### Title @ Company (Start–End) for each role, - for bullets.

Begin the file with a "Changes Made" block:

## Changes Made

- Role detected: {Engineer / PM / EM}
- Summary: [added / rewrote — reason]
- Bullets rewritten: N across M roles (weak verbs → strong verbs)
- [OUTCOME] placeholders added: N bullets (only pure-task bullets — others left as-is)
- Skills section: [added with role structure / reformatted / unchanged]
- [any other changes, e.g. GitHub placeholder added]

Never overwrite the original CV file.

---

Phase 6: Write LinkedIn Improvement Guide

(Skip if the user chose to skip LinkedIn. Write to linkedin-improvements.md in the same directory as the improved CV.)

# LinkedIn Profile Improvements — {Full Name from CV}

Role: {Engineer / PM / EM}
Generated: {today's date}

---

## Headline

**Current:** {their current headline, or "(not set)"}

**Suggested options (pick one or adapt):**
1. {Role-specific option A — uses template from Phase 4a}
2. {Role-specific option B — alternative angle}
3. {Role-specific option C — third variation}

**Why these work better:** {one sentence — what keyword or signal the current headline misses}

---

## About / Summary

**Current status:** {present and strong / present but weak / missing}

**Suggested text (~150–200 words):**

{Write a complete improved About section using the role-specific flow from Phase 4b.
 Line 1–2 (hook, visible before "see more"): lead with role + domain + a hook credential.
 Lines 3–6: core skills, domain expertise, one or two standout achievements from the CV.
 Engineers: include primary stack and a scale-or-system fact (a number, a scope phrase, or a named system — whichever is most credible from the CV).
 PMs: include product type and a business impact fact (numbers carry weight here — use them if the CV has them).
 EMs: include team size and an org achievement.
 Final 2 lines: what they are looking for + contact CTA.}

---

## Experience Description Improvements

{For each role where descriptions are missing or weak, provide improved content.
 Skip roles that are already strong.}

### {Title} @ {Company}

**Add or replace with:**
- {Strong bullet — role-appropriate verb + quantified result drawn from CV content}
- {Additional bullet if the entry was sparse}

---

## Skills to Add or Feature

| Skill | Why | Seen in |
|---|---|---|
| {skill} | {missing from list / should be a top-3 featured skill} | {which role or section} |

---

## CV vs. LinkedIn Inconsistencies

| Field | CV | LinkedIn | Fix |
|---|---|---|---|
{rows — or omit entire table if no inconsistencies found}

---

## Quick Wins — Do These First

Ranked by recruiter visibility impact on LinkedIn for a {role type}:
1. **{Most impactful}** — {what to change and exactly where}
2. **{Second}** — {what to change}
3. **{Third}** — {what to change}

---

Phase 7: Print Summary

## CV & LinkedIn Improvements Complete

Role: {Engineer / PM / EM / Other}

**Improved CV:** {full path to cv-improved.md}
  - Summary: {added fresh / rewrote generic version}
  - Bullets improved: {N} across {M} roles
  - [OUTCOME] placeholders added: {N} (on pure-task bullets only)
  {any other notable changes}

**LinkedIn Guide:** {full path to linkedin-improvements.md}   [or "Skipped"]
  - Headline: {N} alternatives suggested
  - About section: {written fresh / rewrote weak version} (~{word count} words)
  - Experience descriptions: {N} roles with new/improved bullets
  - Inconsistencies flagged: {N}

**Top 3 Quick Wins:**
1. {Most impactful — specific and role-aware}
2. {Second}
3. {Third}