cv-linkedin

---
name: "cv-linkedin"
description: "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."
license: "MIT"
---

# 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.

---

## 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.

---

## 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.

---

## 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)"

---

## 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).

---

## 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".

---

## 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.

---

## 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.)*

```markdown
# 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}
```