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docs(evaluation): reframe onboarding and matching guidance around function, not title (#330)

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Title-lookalike matching collapses a multi-hat career into whichever single
job-title box sounds closest, then searches only inside that box. /setup
Section 9 now asks about the function before collecting search titles,
search-queries.md says to organize priority categories by function with title
variants under each, and 04-job-evaluation.md's Experience dimension matches on
the function and nature of work performed (framework_version 1.2.2 -> 1.2.3).
From discussion #327's field report and calibration example.
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2026-08-16 20:04:42 +02:00
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.claude/commands/setup.md
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@@ -310,7 +310,7 @@ For each reference:
This section generates the search queries that power `/scrape`. Use the information from Sections 1, 4, and 7 to build targeted queries.
Ask about:
- **Role titles to search for:** "What job titles should I search for? For example: Data Scientist, ML Engineer, Geophysicist." Collect 3-8 specific titles.
- **Role titles to search for:** Job titles for the same underlying work vary a lot across companies and markets - a "Data Scientist" role at one employer may be called "Insights Analyst" or "Data Consultant" at another. Ask about the function first: "What kind of work do you actually want to be doing day-to-day?" Then translate that into concrete search terms: "Given that, what job titles should I search for? For example: Data Scientist, ML Engineer, Geophysicist." Collect 3-8 specific titles, but keep the underlying function in mind - it feeds the category naming in `search-queries.md` and the Experience Match dimension in `04-job-evaluation.md`.
- **Key skills as search terms:** "Which of your skills are most likely to appear in job postings?" Pick 3-5 that are distinctive and searchable.
- **Target companies (optional):** "Are there specific companies you'd like to monitor for openings?"
- **Geographic scope:** "Which cities or regions should I search in? How far are you willing to commute?" Use this to define the location filter tiers (ideal, acceptable, borderline, too far).
.claude/skills/job-application-assistant/04-job-evaluation.md
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@@ -1,5 +1,5 @@
---
framework_version: 1.2.2
framework_version: 1.2.3
---
# Job Evaluation Framework
@@ -65,7 +65,7 @@ How well do the required/preferred skills align with the candidate's capabilitie
**Weak match areas:** [SKILLS_YOU_LACK]
### 2. Experience Match (0-100)
Does work history align with what they're looking for?
Does work history align with what they're looking for? Match on the function and nature of the work performed, not the literal job title - a "Data Consultant" and a "Data Scientist" role can be functionally identical.
| Score | Meaning |
|-------|---------|
.claude/skills/job-scraper/search-queries.md
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@@ -25,14 +25,17 @@ Secondary (company career pages via Google):
Queries are grouped by priority. Write **each category in every language from your Languages table** (see Language scope above). Combine each query with your location terms (e.g. your city, region, or metro area) where the site supports it.
**Organize by function, not job title.** The same underlying work carries different titles across companies and markets (a "Data Scientist" role at one employer may be posted as "Insights Analyst" or "Data Consultant" at another). Name each priority category after the function it covers, and list several plausible job titles as query variants within that category rather than betting an entire priority tier on one exact title string.
### Priority 1: [YOUR_PRIMARY_ROLE_TYPE]
These match your strongest and most desired career direction.
```
site:[YOUR_JOB_BOARD] "[YOUR_PRIMARY_JOB_TITLE]" [YOUR_CITY]
site:[YOUR_JOB_BOARD] "[YOUR_PRIMARY_JOB_TITLE_1]" [YOUR_CITY]
site:[YOUR_JOB_BOARD] "[YOUR_PRIMARY_JOB_TITLE_2]" [YOUR_CITY]
site:[YOUR_JOB_BOARD] "[YOUR_KEY_SKILL]" [YOUR_CITY]
site:linkedin.com/jobs "[YOUR_PRIMARY_JOB_TITLE]" [YOUR_COUNTRY]
site:linkedin.com/jobs "[YOUR_PRIMARY_JOB_TITLE_1]" [YOUR_COUNTRY]
```
### Priority 2: [YOUR_DOMAIN_EXPERTISE]