linkedin-search

Search LinkedIn for candidates matching user-defined role, location, and criteria, classify requirements, and save profiles to avoid duplicates.

/linkedin-search — LinkedIn Candidate Search

Automatically search LinkedIn candidates based on user requirements using Chrome DevTools MCP. Saves profiles to ./linkedin-save/{role}/ to prevent duplicate scans.

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Phase 0 — Clarify Requirements Before Searching

Before any PRE-CHECK or search begins, ask the user the following questions if their request does not already answer them clearly. Wait for the user's response before proceeding.

0.1 — Target Count

If not specified:

text

How many candidates are you looking for? (default: 20)

0.2 — Criteria Classification (Hard vs. Soft)

Present the inferred criteria list and ask the user to confirm which are hard requirements and which are soft preferences:

text

I've identified the following candidate criteria from your request.
Please confirm which are hard requirements (must-have, disqualify if missing)
and which are soft preferences (nice-to-have, lower priority but not disqualifying):

Criteria detected:
  • Cloud experience ≥ X years         → [ Hard / Soft / Remove ]
  • Total experience ≤ Y years         → [ Hard / Soft / Remove ]
  • Presales title clearly stated       → [ Hard / Soft / Remove ]
  • Public cloud experience (AWS/GCP…) → [ Hard / Soft / Remove ]
  • Private cloud experience            → [ Hard / Soft / Remove ]
  • [any other criteria from user]      → [ Hard / Soft / Remove ]

Note: Current location and most-recent-role relevance are always verified on the
profile page and cannot be removed — they are built-in hard constraints.

Hard criteria: candidates failing ANY of these are rated C (flagged, not excluded).
Soft criteria: missing ones lower the match level from A → B but do not disqualify.

Reply with your preferred classification, or type "use defaults" to accept the above.

Why ask: The same criterion (e.g., "cloud experience") can mean a dealbreaker to one hiring manager and a nice-to-have to another. Asking upfront prevents the LLM from applying the wrong threshold during profile analysis.

0.3 — Location Scope

If not specified:

text

Should the location filter be strict (exact city only) or broad (country-level)?
Example: "Hanoi only" vs. "anywhere in Vietnam"
(default: country-level)

0.4 — Company Target Filter (Optional)

text

Do you want to target candidates from specific companies?
(e.g., FPT, Viettel, Google, AWS, Oracle — leave blank to skip)

Once all answers are collected, proceed to PRE-CHECK.

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Parameter Layering

The user's natural language query must be split into two layers after Phase 0 and before searching begins:

Layer 1: Search Parameters (hard-embedded in query)

These two types of conditions directly determine the Google/LinkedIn search string. Without them, retrieval is impossible.

Parameter	Description	Example
Job keyword	Must appear in the query	presales solution architect
Location	City or country, must appear in the query	hanoi / vietnam

Built into the search query directly:

text

site:linkedin.com/in/ ("presales" OR "pre-sales") "cloud" "hanoi"

Important: Layer 1 parameters are search hooks — they cast a wide net. They do not guarantee the candidate currently lives in that location or currently holds that type of role. Both must be re-verified on the profile page (see Constraints 1 & 2 in Layer 2 below).

Layer 2: LLM Analysis Parameters (inferred after profile content is retrieved)

These conditions cannot be filtered at the search stage. The LLM reads the profile text and infers each one.

The hard/soft designation for user-defined criteria comes from Phase 0.2. The two location and role constraints below are always Hard.

Parameter	Hard/Soft	Inference Method
Cloud experience (≥ N years)	Hard (configurable)	Sum duration of cloud-related roles
Total experience (≤ N years)	Hard (configurable)	Infer first job start year → years to today
Presales explicitly stated	Hard (configurable)	Job title contains presales/pre-sales/solution consultant, or bio describes it
Current location matches target	Always Hard	Read profile header location — must match target city/country; a past role's location does not count
Most recent role is relevant	Always Hard	Candidate's latest job must relate to the target role — a keyword match in an older position is not sufficient
Public cloud experience	Soft (default)	Role mentions AWS/Azure/GCP/OCI/VNG/Viettel Cloud etc.
Private cloud experience	Soft (default)	Role mentions OpenStack/VMware/private deployment/on-premise cloud

LLM Inference Rules:

• If duration is insufficient to judge, mark unknown; lower match_level to B or C
• [Constraint 1 — Location re-validation]: The search query embeds the target location to surface profiles — but a profile can appear because the location keyword appeared in an old job entry or a past employer's address. After loading the profile, read the location shown in the profile header (e.g., "Hanoi, Vietnam") as the candidate's current location. If it does not match the target, set location_match: false and downgrade match_level one tier (A→B, B→C). If the header location is hidden or empty, mark location_match: unknown and treat as B.
• [Constraint 2 — Current role relevance]: The search query embeds job keywords — but the matching title may be from years ago while the candidate's current work is completely unrelated. After loading the profile, find the first (most recent) entry in the Experience section. Check whether that role's title or description is relevantly related to the target role. If unrelated, set current_role_relevant: false and downgrade match_level one tier. "Relevant" means same domain (e.g., presales, solution architecture, cloud consulting) — an exact title match is not required, but a pivot to an unrelated field (e.g., now a full-time developer or finance manager) is a fail.
• All hard criteria met (including Constraints 1 & 2) → A; some met or one unknown → B; URL only or very little content → C

Match level is driven by the user-confirmed hard/soft split from Phase 0.2. If the user reclassified "presales title" as Soft, a candidate without it can still reach A. However, location match and current-role relevance are always Hard and cannot be reclassified to Soft.

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Runtime Environment Requirements

All conditions below must be met before execution. Complete PRE-CHECK in order before starting the search. If any condition fails, output the fix instructions and stop.

Condition 1: Chrome Debug Session is Running

Chrome must be running in remote debug mode on port 9222, using a user profile with an active LinkedIn session.

Verification: Call mcp__chrome-devtools__list_pages. If it returns a page list, pass.

If not met:

text

❌ Chrome debug session not ready

Run the following steps in your terminal:

1. Close all Chrome windows (or force-quit):
   pkill -9 -f "Google Chrome"

2. Copy your login profile (first time only):
   mkdir -p /tmp/chrome_debug_profile
   cp -r ~/Library/Application\ Support/Google/Chrome/Default/{Cookies,"Login Data","Web Data",Preferences,"Local State"} \
     /tmp/chrome_debug_profile/ 2>/dev/null

3. Launch Chrome in debug mode:
   nohup "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" \
     --remote-debugging-port=9222 \
     --user-data-dir="/tmp/chrome_debug_profile" \
     --no-first-run --disable-sync \
     > /tmp/chrome_debug.log 2>&1 &

4. Wait 3 seconds, then re-run /linkedin-search

Condition 2: LinkedIn is Logged In

The browser must have an active LinkedIn session (used to access profiles and internal search).

Verification: Navigate to https://www.linkedin.com/feed/ and run:

javascript

() => document.querySelector('.global-nav__me-photo') !== null ||
      document.querySelector('[data-control-name="nav.homepage"]') !== null ||
      !document.URL.includes('/login')

Returns true → pass.

If not met:

text

❌ LinkedIn not logged in

Please log in to LinkedIn manually in Chrome:
1. Visit https://www.linkedin.com in the already-open Chrome window
2. Complete login (credentials or verification code)
3. Confirm you reach the LinkedIn feed, then re-run /linkedin-search

Condition 3: linkedin-save Directory Exists

./linkedin-save/ must exist in the current working directory.

Verification:

bash

ls ./linkedin-save/ 2>/dev/null || echo "MISSING"

If not met:

text

❌ linkedin-save directory missing

Auto-creating now... (running mkdir -p ./linkedin-save)

This condition is auto-fixed by the skill — no user action needed.

Condition 4: Correct Working Directory

The skill runs under /Users/junye/project/test-case/linkedin-zp for relative paths to resolve correctly.

Verification:

bash

pwd

If not met:

text

❌ Working directory mismatch

Current: {actual pwd}
Expected: /Users/junye/project/test-case/linkedin-zp

Please reopen the session in the correct directory, or confirm the actual location of linkedin-save.

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PRE-CHECK Execution Order

text

[0] Clarify requirements (Phase 0)    → wait for user response, then continue
[1] Check Chrome debug session        → fail: output fix, stop
[2] Check LinkedIn login              → fail: output fix, stop
[3] Check/create linkedin-save        → auto-fix
[4] Check working directory           → mismatch: warn (do not force stop)
[5] Parse query → output Layer 1 / Layer 2 breakdown
[6] Load already-scanned URL set      → continue
✅ All passed → begin search

[5] Breakdown output example:

text

🔍 Search parameter breakdown:

[Layer 1 — Search Query (hard-embedded)]
  Job keywords: presales solution architect / pre-sales / solution consultant
  Location: hanoi, vietnam
  Company filter: FPT, Viettel (if specified)

[Layer 2 — LLM Analysis Conditions (inferred from profile)]
  Always Hard: current location verified on profile page / most recent role relevance
  Hard: cloud experience ≥ 5 years / total experience ≤ 15 years / presales explicitly stated
  Soft: public cloud preferred / private cloud is a bonus

Target candidates: 20
Save directory: ./linkedin-save/presales-architect/
Location scope: country-level (Vietnam)

---

Execution Steps

All steps below run only after PRE-CHECK (including Phase 0 and parameter breakdown) has fully passed.

STEP 0 — Determine Save Directory, Load Scanned URLs (dedup)

Map job keywords to subdirectory:

Role Type	Directory
Presales Architect	./linkedin-save/presales-architect/
Solution Architect	./linkedin-save/solution-architect/
Cloud Sales	./linkedin-save/cloud-sales/
Other	./linkedin-save/general/

Load already-scanned URLs:

bash

grep -h "^url:" ./linkedin-save/{role_dir}/*.md 2>/dev/null | awk '{print $2}'

STEP 1 — Collect Candidate URLs (Google → LinkedIn internal)

Use Layer 1 parameters (job keywords + location) to build search queries.

Primary: Google site search

Try strategies in order (replace {role} and {location} with actual Layer 1 values):

text

# A: Exact title
site:linkedin.com/in/ ("presales" OR "pre-sales") "cloud" "{location}"

# B: Extended title variants
site:linkedin.com/in/ "solution architect" "cloud" "presales" "{location}"

# C: Target company filter (if user specified companies in Phase 0.4)
site:linkedin.com/in/ ("presales" OR "solution architect") "cloud" ("FPT" OR "Viettel" OR "VNPT" OR "CMC" OR "AWS" OR "Oracle") "{location}"

Paginate: append &start=10, &start=20 to the URL.

Extract Google results JS:

javascript

() => {
  const out = [];
  document.querySelectorAll('h3').forEach(h3 => {
    const block = h3.closest('.g') || h3.closest('[data-hveid]') || h3.closest('[jscontroller]');
    const a = block && block.querySelector('a[href*="linkedin.com/in/"]');
    if (a) out.push({
      url: a.href.split('?')[0].replace(/^https?:\/\/[a-z]+\.linkedin\.com/, 'https://www.linkedin.com'),
      title: h3.innerText,
      snippet: block ? block.innerText.slice(0, 300) : ''
    });
  });
  return out;
}

Fallback: LinkedIn internal search (when Google results are insufficient)

Embed Layer 1 parameters in the URL ({keywords} = job keywords, {geoUrn} = location ID):

text

https://www.linkedin.com/search/results/people/?keywords={keywords}&geoUrn=%5B%22{geoUrn}%22%5D&origin=FACETED_SEARCH&page=N

Common geoUrns:

• Vietnam: 104195383
• Hanoi: 104195383 (country-level, no single-city ID)
• Ho Chi Minh City: 104195383

Extract LinkedIn search results JS:

javascript

async () => {
  const sleep = ms => new Promise(r => setTimeout(r, ms));
  for (let y = 0; y <= 5000; y += 300) { window.scrollTo(0, y); await sleep(150); }
  await sleep(2000);
  return Array.from(document.querySelectorAll('a[href*="/in/"]'))
    .map(a => ({ href: a.href.split('?')[0], text: a.innerText.trim().slice(0, 200) }))
    .filter(l => /linkedin\.com\/in\/[^/]+\/?$/.test(l.href) && l.text.length > 3)
    .filter((v, i, a) => a.findIndex(x => x.href === v.href) === i);
}

STEP 2 — Visit Each Profile (skip already-scanned), LLM Analyzes Layer 2

For each new URL:

1. navigate_page (allow timeout, continue executing)
2. Run scroll + extract script to get raw text:

javascript

async () => {
  const sleep = ms => new Promise(r => setTimeout(r, ms));
  for (let y = 0; y <= 8000; y += 300) { window.scrollTo(0, y); await sleep(200); }
  await sleep(2000);
  const t = document.body.innerText;
  const ei = t.indexOf('Experience');
  return {
    header: t.slice(0, 2500),
    exp: ei > -1 ? t.slice(ei, Math.min(ei + 4000, t.length)) : '',
    url: location.href.split('?')[0]
  };
}

Note: The extraction script searches for 'Experience' (English). If the profile is in another language, also try '工作经历' (Chinese) as a fallback index key.

3. LLM analyzes Layer 2 conditions (inferred from header + exp text):

text

Analyze each criterion below. Mark "unknown" if insufficient data.
Apply the hard/soft classification confirmed by the user in Phase 0.2.

STEP A — Constraint 1: Location re-validation
  Read the location line in the profile header (the line directly under the candidate's name).
  Compare it against the target location.
  → location_match: true / false / unknown

STEP B — Constraint 2: Current role relevance
  Identify the first (topmost) entry in the Experience section — this is the most recent role.
  Check whether its title or description is relevantly related to the target role domain.
  → current_role_relevant: true / false / unknown
  → current_role: "{title} @ {company} ({start}–{end})"

STEP C — User-defined criteria
  - cloud_years: sum duration of roles mentioning AWS/Azure/GCP/OCI/cloud keywords
  - total_years: infer earliest job start year → years to today
  - presales: does job title or bio contain presales/pre-sales/solution consultant?
  - public_cloud: any AWS/Azure/GCP/OCI/public cloud experience?
  - private_cloud: any OpenStack/VMware/private deployment/on-premise experience?

STEP D — match_level
  Start from the highest possible level and downgrade for each failure:
  Base: A
  → If location_match = false or unknown: downgrade one tier
  → If current_role_relevant = false or unknown: downgrade one tier
  → If any Hard user criterion fails or is unknown: downgrade one tier
  → If content is URL-only or nearly empty: set C directly

  Final: A / B / C

4. Save profile immediately → STEP 3

STEP 3 — Save Profile (URL is mandatory; content is best-effort)

File path: ./linkedin-save/{role_dir}/{linkedin_id}.md linkedin_id = last segment of the URL, e.g., nguyen-the-hung-70725b1b

Minimal format (URL only):

markdown

---
name: unknown
url: {full LinkedIn URL}
location: unknown
location_match: unknown
current_role: unknown
current_role_relevant: unknown
cloud_years: unknown
total_years: unknown
public_cloud: unknown
private_cloud: unknown
presales: unknown
match_level: C
scanned_date: {YYYY-MM-DD}
---

Full format (LLM-inferred content):

markdown

---
name: Nguyen The Hung
url: https://www.linkedin.com/in/nguyen-the-hung-70725b1b/
location: Hanoi, Vietnam
location_match: true
current_role: Customer Solution Consultant – GCP @ Google (2022–present)
current_role_relevant: true
cloud_years: 8
total_years: 15
public_cloud: true
private_cloud: true
presales: true
match_level: A
scanned_date: 2026-04-28
---

## Summary
{LLM's 2–3 sentence evaluation of Layer 2 criteria, including explicit notes on
location verification and current role relevance}

## Experience
{Work experience text extracted from the page}

STEP 4 — Loop Until Target Met

Count profiles where match_level is A or B:

bash

grep -l "match_level: [AB]" ./linkedin-save/{role_dir}/*.md 2>/dev/null | wc -l

If target not reached, switch search terms and continue (only vary Layer 1 parameters; Layer 2 criteria stay fixed):

• Job variants: "technical presales" / "pre-sales engineer" / "solution consultant"
• Location stays the same, but may substitute company names: FPT / Viettel / CMC / VNPT / Noventiq

---

Output Summary

When complete, output:

text

## Candidate Summary — {role}, {location}

[Search criteria]  Role: presales solution architect | Location: hanoi
[Analysis criteria] Location verified on profile (Hard) | Current role relevance (Hard)
                    Cloud exp ≥ 5yr (Hard) | Total exp ≤ 15yr (Hard) | Presales stated (Hard) | Public cloud preferred (Soft)

| # | Name | Current Role @ Company | Location | Loc✓ | Role✓ | Cloud Yrs | Presales | Level | Link |
|---|------|------------------------|----------|------|-------|-----------|----------|-------|------|
| 1 | Nguyen The Hung | GCP Consultant @ Google | Hanoi, VN | ✅ | ✅ | 8yr | ✅ | A | [🔗](url) |
| 2 | Tran Van A | Dev Manager @ Bank | HCMC, VN | ❌ | ❌ | 3yr | ❌ | C | [🔗](url) |
...

A: N  |  B: N  |  C: N (needs manual review)
Saved to ./linkedin-save/presales-architect/ (N profiles total)

---

Notes

• URL must always be saved: even if the page is completely blank, save the minimal profile with the URL
• 3rd-degree wall: 3rd-degree connections' experience sections are not visible; LLM infers from header/bio only — mark as C, leave exp empty
• Navigation timeout: on timeout, continue running the scroll script (page may have partially loaded)
• Dedup check: before saving, grep to check if the URL already exists — skip if already scanned
• Rate limiting: natural delay is built in (the scroll script takes ~4 seconds per profile)
• Layer 1 is non-negotiable: location and job keywords must go into the query — post-hoc LLM filtering alone is not sufficient
• Hard/soft classification governs match_level: always apply the classification confirmed in Phase 0.2, not the defaults
• Location re-validation is always required (Constraint 1): the profile header location is ground truth — a past job's city is not the candidate's current location
• Current role relevance is always required (Constraint 2): check the topmost Experience entry, not the one that matched the search keyword — a presales role from 5 years ago does not make a current developer relevant