Job Analyzer

Job Analyzer

When to use

The user shares a single job description, pasted text, a URL, or both, and wants to know if it's worth pursuing.

Workflow

1. Get the Job Description

If a URL is provided, fetch it. If fetching fails or the page is JS-heavy (LinkedIn etc.), ask the user to paste the text.

2. Extract structure

Identify:

• Role title and level signals
• Required skills/experience
• Nice-to-have skills
• Core responsibilities and scope
• Comp range (if listed)
• Location and remote policy
• Team/org context if mentioned

3. Fit assessment

For each significant requirement, rate against my background (from $PERSONAL/career/impact-doc.md). $PERSONAL is the personal-docs sibling directory defined in CLAUDE.md. Resolve it once at the start of the session and reuse.

Use Strong / Moderate / Gap — three buckets, no fence-sitting. Each justification must cite something concrete from my career, not generic claims.

4. Red and green flags

Scan the Job Description's language, not just its requirements:

• Red flags: vague scope, kitchen-sink requirements, "rockstar/ninja," unrealistic stack breadth, no mention of team, comp far below market, urgency cues that suggest churn
• Green flags: clear scope, specific problems to solve, named team/org, reasonable level expectations, signals of engineering maturity (e.g., mentions of design review, on-call rotation structure, mentorship)

5. Verdict

One of:

• Great fit — apply, lead with these strengths: [...]
• Stretch — worth applying if [condition]; will need to address [gap] in conversation
• Poor fit — skip, unless [specific reason to reconsider]

6. If applying, recommend

• Top 3 strengths to lead with (cite specific work from my impact doc)
• Gaps to preempt and how to frame them
• Questions to ask the recruiter/hiring manager to clarify ambiguity in the Job Description

7. Save the role workspace

After producing the analysis, persist it to disk so downstream skills (resume-builder, interview-prep) and future sessions can reference it.

Folder convention: $PERSONAL/applications/<company-slug>/<role-slug>/role.md

Slug rules:

• Lowercase, kebab-case, ASCII-only.
• Company slug: short, canonical form. Apple Inc. → apple. Stripe, Inc. → stripe. Meta Platforms → meta.
• Role slug: title + the distinguishing qualifier, not the full posting title. Staff Software Engineer, Payments Infrastructure → staff-payments-infra. Keep it short enough to skim in the folder tree (≤ 5 words).
• If a slug is ambiguous (e.g., two open Stripe staff roles), ask the user to disambiguate before writing.

If the target file already exists, prompt the user:

• Overwrite — replace the entire file with the new analysis. Use when the prior analysis is outdated or wrong.
• Append — add a new ## Fit analysis (re-run on YYYY-MM-DD) section below the existing one. Default for re-runs after updating impact-doc.md or goals.md. Preserves history.
• Cancel — print the analysis to the terminal only, write nothing.

Tracking state (stage, outcome, date applied, referral, match level) is optional. role.md may mirror these fields when the user provides them inline, but never prompt for them and never invent values. Only write a tracking field when the user has explicitly said something that fills it (e.g., "I applied today" → set date_applied; "recruiter call scheduled" → set stage: recruiter-screen; "Alice referred me" → set referral: Alice Smith). Otherwise omit the field entirely.

The match_level field is the only exception: when first creating role.md from a fit analysis, derive it from the verdict (great-fit / stretch / poor-fit). The user can edit or remove it later.

Schema (single file, YAML frontmatter + body):

---
position: Staff Software Engineer, Payments Infrastructure
company: Stripe
location: San Francisco, CA
work_mode: hybrid                    # remote | hybrid | onsite | unknown
job_id: 6173829                      # if available, else null
link: https://stripe.com/jobs/...    # canonical posting URL
date_added: 2026-05-11               # today's date when first created
comp_range: $280k-$380k TC           # if stated in Job Description, else null
tech_stack: [Ruby, Go, Kafka, Postgres]

# Optional tracking fields. Omit any the user has not provided.
# Only mirror what the user mentions inline; never prompt, never invent.
match_level: great-fit               # great-fit | stretch | poor-fit (derived from verdict on creation)
stage:                               # applied | recruiter-screen | hm-call | tech-screen | onsite | offer | accepted | declined | rejected | withdrawn | closed
outcome:                             # active | accepted | declined | rejected | withdrawn | closed
date_applied:                        # ISO date, only if user has said they applied
referral:                            # name of person who referred, if any
---

## Fit analysis
(the Strong/Moderate/Gap table + red/green flags + verdict + recommendations from steps 3–6)

## Job Description
(full pasted Job Description text, verbatim, so we don't lose it if the URL rots)

## Notes
(empty placeholder for the user's ongoing free-form notes — questions, recruiter context, follow-ups)

After writing, tell the user the absolute path it landed at (resolved from $PERSONAL), e.g. Saved to <absolute>/career-agent-personal-docs/applications/stripe/staff-payments-infra/role.md.

Output format

Concise. Table for the requirements grid, prose for everything else. No filler. End with the verdict in bold so I can scan it. Mention the saved path on the last line.

What not to do

• Don't soften the verdict to be encouraging. If it's a poor fit, say so.
• Don't pad with generic interview tips — that's interview-prep's job.
• Don't generate a resume here — that's resume-builder's job. You can note "this would benefit from a tailored resume emphasizing X" and stop.
• Don't prompt for or invent tracking fields (stage, outcome, date_applied, referral). Only write them when the user has said something inline that fills them.
• Don't auto-overwrite an existing role.md without prompting.