" <level> <location>`. - **Aggregators / niche boards:** LinkedIn Jobs, Wellfound (startups), Otta, We Work Remotely / remote boards, and role-specific boards (e.g. AI, design, data). - **General web:** `"<title>" jobs <location> <recency>` to catch careers pages. Each sub-agent returns **structured candidates** — `title`, `company`, `location/remote`, `url`, `source`, `posted` (if visible), and a one-line why-it-matches. Gather ~8–15 per channel before returning. **Consolidate (main agent):** dedup by company+title+URL; drop obvious mismatches and anything hitting a **hard dealbreaker** (visa/location/comp floor) — note how many were filtered and why. Rank the survivors by a cheap first-pass fit read (title/level/ location/skill keywords vs. profile) — this is triage, not the full score. **Confirm before deep research (human-in-the-loop, cost control):** present the ranked shortlist as a compact table (title @ company · location · source · link · quick-fit note) and ask the user which to fully evaluate — default to the **top ~5–8**. Only the confirmed set goes through Steps 1–3; full research on 30 roles is wasteful and slow. State the shortlist reflects only what these keyless searches surfaced — not the whole market. The confirmed roles then flow into Step 1 exactly like user-provided jobs. ## Step 1 — Fan out: parallel research sub-agents (per role) For each role, spawn the relevant sub-agents **concurrently** (one message, multiple `Agent` calls). Use `subagent_type: "Explore"` (read-only, fast) for fetch-heavy research. Give each the company, role, location, and the user's comp/location targets. Each returns **structured findings** with a `url` + 1–2 sentence `evidence` per claim; mark anything unverified as such. With many roles, do a cheap first-pass triage on the JD text alone and only fan out full research for roles that clear a rough bar (e.g. no hard dealbreaker, plausible seniority match) — note in the brief which roles were triaged out and why. ### Compensation sub-agent - Extract stated comp from the JD. If absent, `WebSearch` for the range (levels.fyi, Glassdoor, Blind, job-board aggregates) for that title/level/location. - **Caveat:** most comp sites are login-walled or JS-heavy — direct `WebFetch` of levels.fyi/Glassdoor/Blind pages usually fails. Prefer numbers that surface in `WebSearch` result snippets and public discussions (HN "who's hiring" threads, Reddit salary threads), and lower the confidence label accordingly. - Return: a best-estimate range + confidence, and how it compares to the user's target. Label estimates clearly as estimates. ### Company-signal sub-agent - `WebSearch` recent signals: funding/stage, growth or layoffs, Glassdoor/Blind sentiment, leadership/team stability, product/market position, notable recent news. - Prefer primary/news sources and real employee threads over SEO content mills ("top companies to work for" listicles carry no signal). - Return: 3–5 signal bullets (positive and negative), each with a source URL. ### Legitimacy sub-agent - Assess whether the posting is real and actively hiring: posting age, whether it's a ghost/evergreen post, reposted-many-times pattern, vague-JD red flags, direct careers-page presence vs. only aggregators. - Return: a legitimacy read (Likely active / Stale / Suspicious) + evidence. ### CV-alignment sub-agent (analysis, not research) - Diff the JD's required + nice-to-have skills against the profile. Return: matched strengths (with the profile evidence), genuine gaps, and JD keywords the profile supports but doesn't currently surface (tailoring candidates — never invent). If a source returns nothing usable, record it as **unknown** — do not fabricate. ## Step 2 — Score (main agent) Score each role **0–10 per dimension**, then a weighted **/5 overall** mapped to a letter. Default weights (tell the user they can override in `jobfit-profile.md`): | Dimension | Weight | What it measures | | ------------------ | ------ | ---------------------------------------------------------- | | Role fit | 30% | Archetype/seniority/responsibility match to the profile | | CV alignment | 25% | Required-skill coverage vs. gaps (from CV-alignment agent) | | Compensation | 20% | Stated/estimated comp vs. the user's target | | Company signal | 15% | Stage, trajectory, sentiment, stability | | Legitimacy/logistics | 10% | Real & active posting + location/remote/visa feasibility | Overall `/5 = (Σ weight × dim/10) × 5`, rounded to one decimal. Letters: **A ≥ 4.5 · B ≥ 4.0 · C ≥ 3.0 · D ≥ 2.0 · F < 2.0.** **Hard dealbreakers** (visa impossible, location impossible, comp far below floor) cap the overall at **C** regardless of other scores, and are called out explicitly. **Verdict per role:** `APPLY` (≥ 4.0) · `MAYBE` (3.0–3.9, list what would move it) · `SKIP` (< 3.0). Mirror career-ops: **discourage applying below 4.0** unless the user has a specific reason, and say so. ## Step 3 — Emit the brief Render this template **in chat**, then save it as `.md` and a self-contained `.html` (see Output files). Cite research inline with linked URLs. ```markdown # JobFit Report **Run:** <YYYY-MM-DD> · **Roles evaluated:** <n> · **Profile:** <source> **Comp target:** <target> · **Location:** <constraints> ## Ranking (best fit first) | # | Role @ Company | Score | Letter | Verdict | |---|----------------|-------|--------|---------| | 1 | ... | 4.2/5 | B+ | APPLY | ## <Role> @ <Company> — <Score>/5 (<Letter>) — <VERDICT> **Link:** <url> · **Location:** <...> · **Posted:** <...> · **Legitimacy:** <...> **Scorecard** - Role fit <x>/10 — <one line> - CV alignment <x>/10 — <matched strengths / key gaps> - Compensation <x>/10 — <stated/estimated vs target> [source] - Company signal <x>/10 — <trajectory/sentiment> [source] - Legitimacy/logistics <x>/10 — <read> **Why this score / what would raise it:** <2–3 sentences> **Tailoring suggestions (reformulate, never fabricate)** - JD asks "<keyword>" → your profile has "<real evidence>" → surface as: "<bullet>" - Gaps to acknowledge or address: <...> (repeat per role, in ranked order) ## Dealbreakers & caveats - <capped roles, unverifiable comp, stale postings, missing profile info> ## Sources Flat list of every cited URL, grouped by role. ``` ### Cover letter (opt-in) If the user asks for one (never by default), draft a short cover letter (≤ 200 words) per APPLY-verdict role, built **strictly** from profile evidence under the same no-fabrication rule — it mirrors the JD's language using only what the profile supports. Save as `out/jobfit/<slug>-cover-letter.md`. Drafting only: it is never sent or submitted anywhere. ## Output files Save to `./out/jobfit/` in the current working directory: - `out/jobfit/<slug>-<YYYY-MM-DD>.md` — the markdown report verbatim. - `out/jobfit/<slug>-<YYYY-MM-DD>.html` — a **bespoke, distinctively designed** report. Self-contained: inline `<style>`, **no JavaScript**, links preserved, scores and scorecards as first-class visual elements, responsive, print-friendly. `<slug>` = for one role, `company-role`; for a batch, `batch`. Lowercase, non-alphanumerics → hyphens. Compute the date with a shell command (`date +%F`) — do not guess. Create `out/jobfit/` if needed; ensure `out/jobfit/` is gitignored (the report may contain personal salary/CV data — never commit it). Tell the user the two saved paths at the end. ### Designing the HTML report Invoke the **`frontend-design`** skill (Skill tool) to design the HTML for this report: - **Brief for the designer:** the subject is *a job seeker's apply/skip decision*; the audience is the candidate scanning "which of these is worth my effort"; the page's one job is to make the ranking, per-role scorecards, verdicts, and tailoring suggestions instantly scannable, with scores and verdicts as first-class visual elements (e.g. score meters, APPLY/MAYBE/SKIP badges). - **Hard constraints (pass these to `frontend-design`):** single self-contained `.html`, **inline CSS only, no JavaScript, no external requests/CDNs/web fonts** (system font stack), keep all source links and scores, readable on mobile and when printed, and **must include every section** with the same content as the `.md`. - Design changes presentation only — never the scores, evidence, or citations. If `frontend-design` is unavailable, fall back to a clean self-contained no-JS layout. ## Guardrails - **Human-in-the-loop.** Evaluate and draft only — **never submit an application**, never fill a form, never send an email on the user's behalf. - **Filtering over volume.** Discourage applying below 4.0/5; a well-targeted 5 beats a generic 50. Say so when a role scores low. - **No fabrication.** Every score → posting text, profile evidence, or a cited source. Reformulate keywords; never invent experience, skills, or metrics. In DISCOVER, never invent job listings — every candidate is a real fetched URL. - **Honest coverage.** DISCOVER surfaces only what keyless searches return; say so and note how many candidates were filtered/dropped — never imply it's the whole market. - **Keyless.** Native `WebSearch`/`WebFetch` + free endpoints only. Never ask for API keys. Degrade gracefully and mark unknowns honestly. - **Privacy.** Reports and the tracker hold personal comp/CV data — save under `out/jobfit/` / `out/jobfit/tracker.md`, keep both gitignored, and never post them externally. - **Tracker is memory, not authority.** Read it to avoid rework; never let an old score silently override fresh evidence, and never edit the user's Status column. - **Estimates are labeled.** Comp ranges pulled from the web are estimates with confidence, not the employer's offer.

--- name: jobfit description: >- Job-fit evaluation and discovery for job seekers — "find roles that fit me" and "should I apply to this one, and how do I tailor for it?" Two modes: DISCOVER searches job boards for openings matching your profile (title/level/location/ remote), and EVALUATE takes job URLs/descriptions you provide. Either way it fans out parallel sub-agents to research compensation, company signal, and posting legitimacy, scores each role A–F across weighted dimensions, and emits a ranked decision brief with tailored CV-bullet suggestions. Remembers every role it has evaluated in out/jobfit/tracker.md so repeat runs skip what you already passed on. Keyless and human-in-the-loop: it never applies for you and discourages low-fit roles. Use when the user wants to find/source matching jobs, evaluate/triage job postings, decide whether a role is worth applying to, compare several openings, or tailor a CV to a JD — e.g. "find jobs that fit my CV", "/jobfit ", "is this job worth applying to", "score these roles against my CV". For general community buzz on a company, use pulse. --- # jobfit — job-fit evaluation & tailoring `/jobfit [url(s) | pasted JD(s)] [profile path/URL or plain-language steering]` Answer one question per role: **is this worth applying to, and if so, how do I tailor for it?** You ground every judgment in the actual posting + light research, score against the user's real profile, and rank by fit — treating job search as a **filtering problem, not a spray-and-pray volume game.** ## Two modes (auto-detect) - **EVALUATE** — the user provides one or more job URLs/JDs. Score and tailor those. - **DISCOVER** — the user provides **no** jobs (e.g. "find jobs that fit my CV"). Source matching openings from the profile, let the user pick, then evaluate those. If jobs are given → EVALUATE. If none are given → DISCOVER. If the user gives jobs but also says "and find more like these," do both: evaluate the given ones and run Step 0b. Adapted from the career-ops philosophy: *"A well-targeted application to 5 companies beats a generic blast to 50."* This skill is deliberately **keyless** (native `WebSearch`/`WebFetch` + free endpoints) and **human-in-the-loop** — it evaluates and drafts, it **never submits**. ## Source-of-truth boundary (read first) User-facing content and scores draw **only** from: 1. the **actual job posting(s)** the user provides, points to, or that DISCOVER surfaces, 2. the user's **profile** (see Step 0), and 3. what the user states **in this session**. **Keywords get reformulated, never fabricated.** Never invent experience, skills, metrics, titles, or achievements the profile doesn't support. Never pull "facts" about the user from memory or cross-session inference. Tailoring = surfacing and rephrasing what's genuinely there to mirror the JD's language. ## The tracker — memory across runs Job search is a multi-week process; this skill must not start from zero every day. Maintain `out/jobfit/tracker.md` in the working directory (**gitignored** — it holds personal data). One row per role ever evaluated: ```markdown | Date | Role @ Company | URL | Score | Verdict | Status | Notes | |------|----------------|-----|-------|---------|--------|-------| | 2026-07-04 | Senior BE @ Acme | | 4.2/5 (B+) | APPLY | new | | ``` `Status` is the user's to update (`new` / `applied` / `interviewing` / `offer` / `rejected` / `passed`) — never change it yourself, but read it. - **At the start of every run:** read the tracker if it exists. - **DISCOVER:** dedup candidates against the tracker before building the shortlist. Drop roles previously verdicted SKIP (mention the count); mark previously seen roles as "seen , scored " instead of re-researching them — unless the user explicitly asks for a re-evaluation. - **EVALUATE:** if a role is already in the tracker, note the previous score in the brief and call out what changed (reposted? comp updated? profile updated?). - **At the end of every run:** append the newly evaluated roles to the tracker (create the file if missing, keep it sorted newest-first) and tell the user it was updated. Never overwrite user-edited `Status`/`Notes` cells. ## Step 0 — Resolve the profile & inputs **Profile.** In priority order: 1. A `jobfit-profile.md` or `cv.md` in the current working directory (check for it). 2. A source the user gives — a **local path** (`/jobfit ./my-cv.md`) **or a URL** (LinkedIn, personal site, a hosted/Google-Doc CV). For a URL, `WebFetch` it, parse it into the fields below, and **save the result as `jobfit-profile.md`** in the working directory so future runs reuse it (tell the user where you saved it). If the URL is login-walled or unfetchable, say so and ask the user to paste it instead. 3. If none of the above, ask the user to paste their CV / a short profile. Offer to save it as `jobfit-profile.md` for next time. When parsing a fetched/pasted profile, extract **only what's actually stated** — never infer or embellish. From the profile, extract (and confirm if thin): target titles/archetype, seniority, core skills, notable achievements *with metrics*, comp target (base/total, currency), location + remote/timezone constraints, visa/work-authorization needs, and any must-haves / dealbreakers. Record what's **missing** — gaps become scoring inputs and questions, never fabrications. **Jobs.** Accept one or many. For each URL, `WebFetch` the posting and extract: title, company, location/remote, seniority, responsibilities, required vs. nice-to-have skills, stated comp (if any), and posting date. For pasted JDs, parse directly. If a posting can't be fetched (login-walled/expired), say so and ask the user to paste it. **If no jobs were given, go to Step 0b (DISCOVER).** Ask **1–2 questions only if** genuinely blocking (e.g. no comp target at all, or the role archetype is ambiguous). Otherwise proceed and state assumptions in the brief. ## Step 0b — Discover roles (DISCOVER mode) Source openings that match the profile, then let the user choose which to evaluate. Discovery is **keyless** — `WebSearch`/`WebFetch` only, no scraping behind logins. **Build the search terms from the profile:** target title(s) + close variants, seniority/level, location, remote/timezone, and 2–3 signature skills. Honor any plain-language steering ("only remote", "startups", "in Berlin", "focus on AI infra"). **Fan out discovery sub-agents concurrently** (one message, multiple `Agent` calls, `subagent_type: "Explore"`) — each searches a different channel so coverage isn't one search engine's opinion. Suggested channels (skip any that return nothing): - **ATS boards** (where real reqs live): `WebSearch` with `site:` filters — `site:boards.greenhouse.io`, `site:jobs.ashbyhq.com`, `site:jobs.lever.co`, `site:*.workday*.com` — combined with `"" <level> <location>`. - **Aggregators / niche boards:** LinkedIn Jobs, Wellfound (startups), Otta, We Work Remotely / remote boards, and role-specific boards (e.g. AI, design, data). - **General web:** `"<title>" jobs <location> <recency>` to catch careers pages. Each sub-agent returns **structured candidates** — `title`, `company`, `location/remote`, `url`, `source`, `posted` (if visible), and a one-line why-it-matches. Gather ~8–15 per channel before returning. **Consolidate (main agent):** dedup by company+title+URL; drop obvious mismatches and anything hitting a **hard dealbreaker** (visa/location/comp floor) — note how many were filtered and why. Rank the survivors by a cheap first-pass fit read (title/level/ location/skill keywords vs. profile) — this is triage, not the full score. **Confirm before deep research (human-in-the-loop, cost control):** present the ranked shortlist as a compact table (title @ company · location · source · link · quick-fit note) and ask the user which to fully evaluate — default to the **top ~5–8**. Only the confirmed set goes through Steps 1–3; full research on 30 roles is wasteful and slow. State the shortlist reflects only what these keyless searches surfaced — not the whole market. The confirmed roles then flow into Step 1 exactly like user-provided jobs. ## Step 1 — Fan out: parallel research sub-agents (per role) For each role, spawn the relevant sub-agents **concurrently** (one message, multiple `Agent` calls). Use `subagent_type: "Explore"` (read-only, fast) for fetch-heavy research. Give each the company, role, location, and the user's comp/location targets. Each returns **structured findings** with a `url` + 1–2 sentence `evidence` per claim; mark anything unverified as such. With many roles, do a cheap first-pass triage on the JD text alone and only fan out full research for roles that clear a rough bar (e.g. no hard dealbreaker, plausible seniority match) — note in the brief which roles were triaged out and why. ### Compensation sub-agent - Extract stated comp from the JD. If absent, `WebSearch` for the range (levels.fyi, Glassdoor, Blind, job-board aggregates) for that title/level/location. - **Caveat:** most comp sites are login-walled or JS-heavy — direct `WebFetch` of levels.fyi/Glassdoor/Blind pages usually fails. Prefer numbers that surface in `WebSearch` result snippets and public discussions (HN "who's hiring" threads, Reddit salary threads), and lower the confidence label accordingly. - Return: a best-estimate range + confidence, and how it compares to the user's target. Label estimates clearly as estimates. ### Company-signal sub-agent - `WebSearch` recent signals: funding/stage, growth or layoffs, Glassdoor/Blind sentiment, leadership/team stability, product/market position, notable recent news. - Prefer primary/news sources and real employee threads over SEO content mills ("top companies to work for" listicles carry no signal). - Return: 3–5 signal bullets (positive and negative), each with a source URL. ### Legitimacy sub-agent - Assess whether the posting is real and actively hiring: posting age, whether it's a ghost/evergreen post, reposted-many-times pattern, vague-JD red flags, direct careers-page presence vs. only aggregators. - Return: a legitimacy read (Likely active / Stale / Suspicious) + evidence. ### CV-alignment sub-agent (analysis, not research) - Diff the JD's required + nice-to-have skills against the profile. Return: matched strengths (with the profile evidence), genuine gaps, and JD keywords the profile supports but doesn't currently surface (tailoring candidates — never invent). If a source returns nothing usable, record it as **unknown** — do not fabricate. ## Step 2 — Score (main agent) Score each role **0–10 per dimension**, then a weighted **/5 overall** mapped to a letter. Default weights (tell the user they can override in `jobfit-profile.md`): | Dimension | Weight | What it measures | | ------------------ | ------ | ---------------------------------------------------------- | | Role fit | 30% | Archetype/seniority/responsibility match to the profile | | CV alignment | 25% | Required-skill coverage vs. gaps (from CV-alignment agent) | | Compensation | 20% | Stated/estimated comp vs. the user's target | | Company signal | 15% | Stage, trajectory, sentiment, stability | | Legitimacy/logistics | 10% | Real & active posting + location/remote/visa feasibility | Overall `/5 = (Σ weight × dim/10) × 5`, rounded to one decimal. Letters: **A ≥ 4.5 · B ≥ 4.0 · C ≥ 3.0 · D ≥ 2.0 · F < 2.0.** **Hard dealbreakers** (visa impossible, location impossible, comp far below floor) cap the overall at **C** regardless of other scores, and are called out explicitly. **Verdict per role:** `APPLY` (≥ 4.0) · `MAYBE` (3.0–3.9, list what would move it) · `SKIP` (< 3.0). Mirror career-ops: **discourage applying below 4.0** unless the user has a specific reason, and say so. ## Step 3 — Emit the brief Render this template **in chat**, then save it as `.md` and a self-contained `.html` (see Output files). Cite research inline with linked URLs. ```markdown # JobFit Report **Run:** <YYYY-MM-DD> · **Roles evaluated:** <n> · **Profile:** <source> **Comp target:** <target> · **Location:** <constraints> ## Ranking (best fit first) | # | Role @ Company | Score | Letter | Verdict | |---|----------------|-------|--------|---------| | 1 | ... | 4.2/5 | B+ | APPLY | ## <Role> @ <Company> — <Score>/5 (<Letter>) — <VERDICT> **Link:** <url> · **Location:** <...> · **Posted:** <...> · **Legitimacy:** <...> **Scorecard** - Role fit <x>/10 — <one line> - CV alignment <x>/10 — <matched strengths / key gaps> - Compensation <x>/10 — <stated/estimated vs target> [source] - Company signal <x>/10 — <trajectory/sentiment> [source] - Legitimacy/logistics <x>/10 — <read> **Why this score / what would raise it:** <2–3 sentences> **Tailoring suggestions (reformulate, never fabricate)** - JD asks "<keyword>" → your profile has "<real evidence>" → surface as: "<bullet>" - Gaps to acknowledge or address: <...> (repeat per role, in ranked order) ## Dealbreakers & caveats - <capped roles, unverifiable comp, stale postings, missing profile info> ## Sources Flat list of every cited URL, grouped by role. ``` ### Cover letter (opt-in) If the user asks for one (never by default), draft a short cover letter (≤ 200 words) per APPLY-verdict role, built **strictly** from profile evidence under the same no-fabrication rule — it mirrors the JD's language using only what the profile supports. Save as `out/jobfit/<slug>-cover-letter.md`. Drafting only: it is never sent or submitted anywhere. ## Output files Save to `./out/jobfit/` in the current working directory: - `out/jobfit/<slug>-<YYYY-MM-DD>.md` — the markdown report verbatim. - `out/jobfit/<slug>-<YYYY-MM-DD>.html` — a **bespoke, distinctively designed** report. Self-contained: inline `<style>`, **no JavaScript**, links preserved, scores and scorecards as first-class visual elements, responsive, print-friendly. `<slug>` = for one role, `company-role`; for a batch, `batch`. Lowercase, non-alphanumerics → hyphens. Compute the date with a shell command (`date +%F`) — do not guess. Create `out/jobfit/` if needed; ensure `out/jobfit/` is gitignored (the report may contain personal salary/CV data — never commit it). Tell the user the two saved paths at the end. ### Designing the HTML report Invoke the **`frontend-design`** skill (Skill tool) to design the HTML for this report: - **Brief for the designer:** the subject is *a job seeker's apply/skip decision*; the audience is the candidate scanning "which of these is worth my effort"; the page's one job is to make the ranking, per-role scorecards, verdicts, and tailoring suggestions instantly scannable, with scores and verdicts as first-class visual elements (e.g. score meters, APPLY/MAYBE/SKIP badges). - **Hard constraints (pass these to `frontend-design`):** single self-contained `.html`, **inline CSS only, no JavaScript, no external requests/CDNs/web fonts** (system font stack), keep all source links and scores, readable on mobile and when printed, and **must include every section** with the same content as the `.md`. - Design changes presentation only — never the scores, evidence, or citations. If `frontend-design` is unavailable, fall back to a clean self-contained no-JS layout. ## Guardrails - **Human-in-the-loop.** Evaluate and draft only — **never submit an application**, never fill a form, never send an email on the user's behalf. - **Filtering over volume.** Discourage applying below 4.0/5; a well-targeted 5 beats a generic 50. Say so when a role scores low. - **No fabrication.** Every score → posting text, profile evidence, or a cited source. Reformulate keywords; never invent experience, skills, or metrics. In DISCOVER, never invent job listings — every candidate is a real fetched URL. - **Honest coverage.** DISCOVER surfaces only what keyless searches return; say so and note how many candidates were filtered/dropped — never imply it's the whole market. - **Keyless.** Native `WebSearch`/`WebFetch` + free endpoints only. Never ask for API keys. Degrade gracefully and mark unknowns honestly. - **Privacy.** Reports and the tracker hold personal comp/CV data — save under `out/jobfit/` / `out/jobfit/tracker.md`, keep both gitignored, and never post them externally. - **Tracker is memory, not authority.** Read it to avoid rework; never let an old score silently override fresh evidence, and never edit the user's Status column. - **Estimates are labeled.** Comp ranges pulled from the web are estimates with confidence, not the employer's offer.