" <CITY>`, treat results without a visible date as Leads, and tell the user once: "Indeed connector unavailable — used web search instead. Fix: connect Indeed at claude.ai → Settings → Connectors, then restart Claude Code." - **LinkedIn**: no public API, and scraping violates its terms, so use web search with `site:` queries and freshness words from the playbook. - **Naukri**: same approach, using `site:naukri.com/job-listings` queries with the experience band. - **Instahyre**: follow `references/instahyre-hirist.md` — `site:instahyre.com/job-` queries plus the matching listing page (e.g. `instahyre.com/<title>-jobs-in-<city>/`). Pages are JavaScript-rendered, so posting dates may be missing; handle as described there. - **Hirist**: follow `references/instahyre-hirist.md` — `site:hirist.tech/j` queries plus the matching listing page (e.g. `hirist.tech/<skill>-jobs-in-<city>`); read company, city and experience from the job description header. - **Job-alert emails** (if an email connector is enabled): read LinkedIn, Naukri, Instahyre and Hirist alert emails from the last 48 hours and process their job links. See `references/instahyre-hirist.md`. - Never log in, scrape, or automate a user account on any of these sites. - **Workday**: search `site:myworkdayjobs.com` for each target title + location. Each company has its own Workday tenant. - **Pacing**: follow `references/rate-limits.md`; fetch each site's pages one at a time. ## Step 3 — Verify freshness and fit For each candidate result: 1. **Verified vs Leads.** Verified = a date or relative age ("5 hours ago", "1 day ago", "Posted Yesterday") within 48 hours, read from the fetched page, connector data or an alert email. Only Verified jobs go in the main table. Never infer freshness from search ranking, fit score or "Apply now" text. Show the exact age in the Posted column (e.g. "5h", "1d", "2d"). Jobs without a verifiable date are Leads: apply the stale-ID rules (`references/instahyre-hirist.md`), drop any "likely older" lead, keep max 5 leads, newest ID first. 2. Classify the employer using `references/company-filter.md` (Product, Product (startup), GCC, Services, Staffing, Unverified). Drop Services and Staffing, and drop agency posts that don't name the product company. Apply `allow_companies` / `block_companies` from the profile. 3. Drop postings whose experience requirement clearly excludes the candidate (e.g. "8+ years", "Fresher only"). 4. Deduplicate the same role posted on several platforms; keep the direct employer / Workday link first, list the others as alternates. 5. Score quick fit 0–100: title match (30), must-have skill overlap (40), experience band (15), location (15). ### Fetch errors (handle per link, not per site) - 404/410 or "job no longer available" → job removed/filled → drop it (not a lead). - Single 403 → skip that link, keep the job as a Lead from its snippet/URL, continue. - Any 429 or Cloudflare/bot challenge → stop fetching that site for the rest of the run, no retries (blocks last ~1 hour), write the time to `job-hunt/cooldown.md`, skip that site for 90 minutes. Never try to bypass a challenge. - Also stop a site after 3 consecutive 403s or a robots.txt refusal. - Naukri refuses automated fetching via robots.txt: use snippets, pasted links and alert emails; never fetch Naukri job pages directly. ## Step 4 — Present and save Present two sections: - **Fresh jobs (verified ≤ 48h)**: table sorted by fit then newest, columns #, Role, Company, Company type, Location, Exp asked, Posted (e.g. "5h", "1d"), Source, Fit, Link. Keep it to the top 15 unless the user asks for more. If empty, say so plainly; never fill it with unverified jobs. - **Leads (posting date unknown, may be older)**: max 5, labeled "check the posted date before applying", never mixed into or ranked above the main table. Above the Fresh jobs table, show the per-source summary line (see Rules). Below both sections, one line on anything notable (e.g. "3 roles mention Kafka, which is not on your resume") and a count of how many services/agency postings were filtered out, so the user knows the filter ran. Save the full list to `job-hunt/jobs-YYYY-MM-DD.md` (including the JD text fetched for each), so `/tailor-resume` can reuse it without refetching. End by offering: "Pick job numbers and I'll build the apply pack for each." Do not submit applications on the user's behalf. ## Rules - Never fabricate a job, company, link or posting date. Every row needs a URL that was actually returned by a tool. - Always print a per-source summary line for all six sources — Indeed, LinkedIn, Naukri, Instahyre, Hirist, Workday — with found → kept counts, even when a source returns zero. - Respect platform terms: search-engine results and user-pasted links only for non-API sites. 23:o9,28:T1975,23:o1975,--- name: tailor-resume description: > This skill should be used when the user asks to "tailor my resume", "customize resume for this job", "make my resume ATS friendly", "add keywords from this JD", "optimize resume for this role", "check my ATS score", or runs /tailor-resume with a job link, job number or pasted job description. It rewrites the master resume for one job and outputs an ATS-safe DOCX and PDF plus a keyword match report. metadata: version: "0.1.0" --- # Tailor Resume for One Job Produce a job-specific, ATS-friendly resume from the user's master resume, without inventing anything. Output: `Firstname_Lastname_<Company>_<Role>.docx` and `.pdf`, and a match report. ## Inputs 1. **Job**: a job number from the latest `job-hunt/jobs-*.md`, a URL (fetch it), or pasted JD text. 2. **Master resume**: path recorded in `job-hunt/profile.md`, else ask for it. Treat the master resume as the single source of truth. Never edit the master file itself. ## Step 0 — Check how the master resume parses (first run, and whenever it changes) 1. Extract text in plain reading order (e.g. `pdftotext file.pdf -` without `-layout`) and also with another extractor (e.g. pypdf). ATS parsers read in stream order, not visual order. 2. Flag: sentences ending mid-phrase ("by", "from", "preventing"), empty bullets, fragments collected at the end of the text, skills lines with no items, text in headers/footers or text boxes, mixed font sets that indicate text was edited directly inside the PDF, hyphenated keywords split at a line wrap ("eventdriven", "sub200ms", "event- driven"), and private-use bullet glyphs (U+F0B7). Run the same check on every generated PDF before delivering it. 3. If problems exist, reconstruct the true content from the visual layout (`pdftotext -layout`), show the user what an ATS currently sees, and offer a clean rebuild of the master with the builder, with no wording changes. Use the rebuilt file as the master from then on. ## Step 0.5 — Learn the master's style Record in `job-hunt/profile.md`: section names and order, title-line format, bullet pattern, and the metrics used. Preserve all of it when tailoring. For outcome/impact-style resumes, every bullet keeps the shape **Action verb + what was built/led + key tech → outcome → measurable business impact** (latency, throughput, uptime, users, ₹ volume, GMV, success rate, cost, MTTR). When rewording for a JD, change the tech/keyword phrasing and ordering, never remove or weaken the metric. ## Step 1 — Analyse the JD Read `references/keyword-extraction.md`. Extract into three buckets: - **Must-have** (in requirements / "required" / repeated ≥2 times / in the title) - **Nice-to-have** (preferred, "plus", "good to have") - **Context terms** (domain, methodologies, tools, soft-skill phrases the JD uses) Record the exact spelling the JD uses (e.g. "Node.js" vs "NodeJS", "CI/CD", "Power BI"). ## Step 2 — Map against the master resume For every keyword, classify: - **Present** — already in the resume with the same wording. - **Present, different wording** — the candidate has it but phrased differently (e.g. resume says "AWS Lambda functions", JD says "serverless") → rephrase to include the JD term. - **Adjacent** — related experience exists (e.g. JD wants GCP, resume has AWS) → do NOT claim the keyword; mention it in the gap report with a suggested honest line ("cloud platforms (AWS; GCP exposure)" only if the user confirms GCP exposure). - **Missing** — no evidence → list in gap report. Never add to the resume. Before writing, if any Adjacent/Missing item is a must-have, ask the user once, in a single message, whether they have that experience. Add only what they confirm. ## Step 3 — Rewrite Follow `references/ats-rules.md` strictly. In order: 1. **Headline + Summary** (3–4 lines): mirror the JD's job title if truthful, state total experience exactly as in profile (e.g. "4.11 years" → write "~5 years" only if the user approves; default "4+ years"), 4–6 must-have keywords, one quantified outcome. 2. **Skills**: grouped lines (Languages, Frameworks, Cloud/DevOps, Data, Tools — adapt to domain), JD must-haves first, JD spelling, no skill bars or ratings. 3. **Experience**: keep every role, company and date exactly as in master. Reorder and rewrite bullets so the most JD-relevant are first; each bullet = action verb + what + tech/keyword + measurable result. Keep numbers from the master; never invent metrics — if a bullet lacks one, ask or leave it qualitative. 4–6 bullets for the current role, 2–4 for older ones. 4. **Projects / Achievements / Certifications / Education**: keep every item; reorder projects so the most JD-relevant come first; keep the master's section names (e.g. Key Projects). 5. Length: keep the master's page count (1 page if the master is 1 page). ## Step 4 — Build the files Write the tailored content as JSON matching `scripts/resume_schema.md`, then run: ```bash python3 ${CLAUDE_PLUGIN_ROOT}/skills/tailor-resume/scripts/build_resume.py content.json job-hunt/output/<Name>_<Company>_<Role> ``` This creates the DOCX (single column, standard headings, Calibri 10.5–11pt, no tables/text boxes/headers/footers/images) and converts it to PDF with LibreOffice. If python-docx or LibreOffice is missing, install python-docx with pip; if PDF conversion still fails, fall back to the environment's docx and pdf skills and tell the user. ## Step 5 — Verify and report 1. Run the ATS check on the generated PDF and fix every FAIL before delivering: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/tailor-resume/scripts/ats_check.py <pdf> --pages <master page count> --keywords "<JD must-haves the resume truthfully contains>"` Also run it on the master resume in Step 0. 2. Compute the keyword match score = matched must-haves ×2 + matched nice-to-haves, as a % of the maximum. Report before → after. 3. Present the match report (see `references/keyword-extraction.md` for format): score, keywords added, keywords still missing with honest suggestions (course, small project, or how to address in interview). 4. 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It runs the full flow: find fresh jobs (or take the chosen ones), tailor the resume, and write a match report, cover letter and recruiter outreach message for each selected job. metadata: version: "0.1.0" --- # Apply Pack (end-to-end) Produce, per selected job, a ready-to-submit folder: `job-hunt/output/_/` containing 1. Tailored resume — DOCX + PDF 2. `match-report.md` — keyword score before → after, gaps, apply/skip recommendation 3. `cover-letter.docx` + `.pdf` 4. `outreach.md` — LinkedIn connection note, LinkedIn InMail/DM, and short email to recruiter ## Flow 1. **Jobs**: if the user gave job numbers, links or JDs, use them. Otherwise run the `find-jobs` skill first and ask which numbers to process (suggest the top 3–5 by fit). 2. **Resume**: for each job, run the `tailor-resume` skill end to end. Ask the gap-confirmation question once for all selected jobs together, not once per job. 3. **Skip low fit**: if a job's must-have match stays below 50% after tailoring, still produce the match report but ask before producing the letter and messages. 4. **Cover letter** and **outreach**: write using `references/writing-guide.md`, drawing only on facts in the tailored resume and the JD. 5. **Summary**: finish with a table — Company, Role, Match % after, Recommendation, Link — and a short checklist: upload PDF (or DOCX for Workday if the parse preview looks wrong), check the portal's auto-filled fields, update Naukri headline/Key Skills if the role family changed. Never submit applications, send messages, or log into job portals on the user's behalf. The user reviews and sends everything. 23:o9,27:T1fbd,23:o1fbd,--- name: find-jobs description: > This skill should be used when the user asks to "find jobs", "find fresh jobs", "search new openings", "show me jobs posted today", "jobs from the last 2 days", "look for roles on Naukri / LinkedIn / Indeed / Instahyre / Hirist / Workday", "any new jobs for me", or runs /find-jobs. It finds job postings from the last 48 hours at product-based companies that match the user's master resume. metadata: version: "0.1.0" --- # Find Fresh Jobs (last 48 hours) Find job postings published in the last 48 hours at product-based companies (not IT services, consulting or staffing agencies) that fit the user's master resume, across Indeed, LinkedIn, Naukri, Instahyre, Hirist and company Workday career sites. Return a ranked shortlist with working links. ## Step 1 — Load the candidate profile 1. Look in the working folder for `job-hunt/profile.md`. If missing, seed it from `references/default-profile.md` and confirm it with the user. If it exists, read it and skip to Step 2 (confirm in one line: "Searching for in "). 2. If it does not exist, get the master resume: an uploaded file (PDF/DOCX), a file in the working folder, or the user's Indeed resume via the Indeed connector's resume tool. Ask for it only if none of these exist. 3. Extract and write `job-hunt/profile.md` with: target titles (2–4, derived from current title and trajectory), total experience (e.g. 4.11 years → search band 3–6 years), core skills (top 10–15), domain, preferred locations (default: current city + Remote), notice period and expected CTC if stated, plus the company settings from `references/company-filter.md` (default: product only, GCCs included). Show the profile to the user in a few lines and ask one question only if target titles or location are genuinely unclear. ## Step 2 — Search all sources in parallel Read `references/search-playbook.md` for exact query patterns. Run the sources independently, not as one combined query. - **In Claude Code**: in ONE message launch one `source-scout` subagent per web source (LinkedIn, Naukri, Instahyre, Hirist, Workday) with the source name, candidate profile, current date/time and plugin root path. Run the Indeed connector searches in the main session at the same time. Merge each scout's returned JSON into the candidate list. - **No subagents available** (e.g. Cowork) or the user says "don't use subagents": run all search queries for all sources in parallel, then fetch job pages in rounds of at most ONE page per site per round, all sites in parallel within a round, respecting each site's delay in `references/rate-limits.md`. - Skip any site currently in cooldown — see `job-hunt/cooldown.md`. ### Source details (used by scouts and fallback path) - **Indeed**: use the user's claude.ai Indeed connector's job search tool (load it with tool search if deferred). Filter to last 3 days (Indeed has no 2-day filter), then drop anything older than 48 hours in Step 3, each target title × each location. If the connector's tools are missing, fail to authenticate, or error, do NOT stall or retry: fall back to web search `site:in.indeed.com/viewjob "" <CITY>`, treat results without a visible date as Leads, and tell the user once: "Indeed connector unavailable — used web search instead. Fix: connect Indeed at claude.ai → Settings → Connectors, then restart Claude Code." - **LinkedIn**: no public API, and scraping violates its terms, so use web search with `site:` queries and freshness words from the playbook. - **Naukri**: same approach, using `site:naukri.com/job-listings` queries with the experience band. - **Instahyre**: follow `references/instahyre-hirist.md` — `site:instahyre.com/job-` queries plus the matching listing page (e.g. `instahyre.com/<title>-jobs-in-<city>/`). Pages are JavaScript-rendered, so posting dates may be missing; handle as described there. - **Hirist**: follow `references/instahyre-hirist.md` — `site:hirist.tech/j` queries plus the matching listing page (e.g. `hirist.tech/<skill>-jobs-in-<city>`); read company, city and experience from the job description header. - **Job-alert emails** (if an email connector is enabled): read LinkedIn, Naukri, Instahyre and Hirist alert emails from the last 48 hours and process their job links. See `references/instahyre-hirist.md`. - Never log in, scrape, or automate a user account on any of these sites. - **Workday**: search `site:myworkdayjobs.com` for each target title + location. Each company has its own Workday tenant. - **Pacing**: follow `references/rate-limits.md`; fetch each site's pages one at a time. ## Step 3 — Verify freshness and fit For each candidate result: 1. **Verified vs Leads.** Verified = a date or relative age ("5 hours ago", "1 day ago", "Posted Yesterday") within 48 hours, read from the fetched page, connector data or an alert email. Only Verified jobs go in the main table. Never infer freshness from search ranking, fit score or "Apply now" text. Show the exact age in the Posted column (e.g. "5h", "1d", "2d"). Jobs without a verifiable date are Leads: apply the stale-ID rules (`references/instahyre-hirist.md`), drop any "likely older" lead, keep max 5 leads, newest ID first. 2. Classify the employer using `references/company-filter.md` (Product, Product (startup), GCC, Services, Staffing, Unverified). Drop Services and Staffing, and drop agency posts that don't name the product company. Apply `allow_companies` / `block_companies` from the profile. 3. Drop postings whose experience requirement clearly excludes the candidate (e.g. "8+ years", "Fresher only"). 4. Deduplicate the same role posted on several platforms; keep the direct employer / Workday link first, list the others as alternates. 5. Score quick fit 0–100: title match (30), must-have skill overlap (40), experience band (15), location (15). ### Fetch errors (handle per link, not per site) - 404/410 or "job no longer available" → job removed/filled → drop it (not a lead). - Single 403 → skip that link, keep the job as a Lead from its snippet/URL, continue. - Any 429 or Cloudflare/bot challenge → stop fetching that site for the rest of the run, no retries (blocks last ~1 hour), write the time to `job-hunt/cooldown.md`, skip that site for 90 minutes. Never try to bypass a challenge. - Also stop a site after 3 consecutive 403s or a robots.txt refusal. - Naukri refuses automated fetching via robots.txt: use snippets, pasted links and alert emails; never fetch Naukri job pages directly. ## Step 4 — Present and save Present two sections: - **Fresh jobs (verified ≤ 48h)**: table sorted by fit then newest, columns #, Role, Company, Company type, Location, Exp asked, Posted (e.g. "5h", "1d"), Source, Fit, Link. Keep it to the top 15 unless the user asks for more. If empty, say so plainly; never fill it with unverified jobs. - **Leads (posting date unknown, may be older)**: max 5, labeled "check the posted date before applying", never mixed into or ranked above the main table. Above the Fresh jobs table, show the per-source summary line (see Rules). Below both sections, one line on anything notable (e.g. "3 roles mention Kafka, which is not on your resume") and a count of how many services/agency postings were filtered out, so the user knows the filter ran. Save the full list to `job-hunt/jobs-YYYY-MM-DD.md` (including the JD text fetched for each), so `/tailor-resume` can reuse it without refetching. End by offering: "Pick job numbers and I'll build the apply pack for each." Do not submit applications on the user's behalf. ## Rules - Never fabricate a job, company, link or posting date. Every row needs a URL that was actually returned by a tool. - Always print a per-source summary line for all six sources — Indeed, LinkedIn, Naukri, Instahyre, Hirist, Workday — with found → kept counts, even when a source returns zero. - Respect platform terms: search-engine results and user-pasted links only for non-API sites. 23:o9,28:T1975,23:o1975,--- name: tailor-resume description: > This skill should be used when the user asks to "tailor my resume", "customize resume for this job", "make my resume ATS friendly", "add keywords from this JD", "optimize resume for this role", "check my ATS score", or runs /tailor-resume with a job link, job number or pasted job description. It rewrites the master resume for one job and outputs an ATS-safe DOCX and PDF plus a keyword match report. metadata: version: "0.1.0" --- # Tailor Resume for One Job Produce a job-specific, ATS-friendly resume from the user's master resume, without inventing anything. Output: `Firstname_Lastname_<Company>_<Role>.docx` and `.pdf`, and a match report. ## Inputs 1. **Job**: a job number from the latest `job-hunt/jobs-*.md`, a URL (fetch it), or pasted JD text. 2. **Master resume**: path recorded in `job-hunt/profile.md`, else ask for it. Treat the master resume as the single source of truth. Never edit the master file itself. ## Step 0 — Check how the master resume parses (first run, and whenever it changes) 1. Extract text in plain reading order (e.g. `pdftotext file.pdf -` without `-layout`) and also with another extractor (e.g. pypdf). ATS parsers read in stream order, not visual order. 2. Flag: sentences ending mid-phrase ("by", "from", "preventing"), empty bullets, fragments collected at the end of the text, skills lines with no items, text in headers/footers or text boxes, mixed font sets that indicate text was edited directly inside the PDF, hyphenated keywords split at a line wrap ("eventdriven", "sub200ms", "event- driven"), and private-use bullet glyphs (U+F0B7). Run the same check on every generated PDF before delivering it. 3. If problems exist, reconstruct the true content from the visual layout (`pdftotext -layout`), show the user what an ATS currently sees, and offer a clean rebuild of the master with the builder, with no wording changes. Use the rebuilt file as the master from then on. ## Step 0.5 — Learn the master's style Record in `job-hunt/profile.md`: section names and order, title-line format, bullet pattern, and the metrics used. Preserve all of it when tailoring. For outcome/impact-style resumes, every bullet keeps the shape **Action verb + what was built/led + key tech → outcome → measurable business impact** (latency, throughput, uptime, users, ₹ volume, GMV, success rate, cost, MTTR). When rewording for a JD, change the tech/keyword phrasing and ordering, never remove or weaken the metric. ## Step 1 — Analyse the JD Read `references/keyword-extraction.md`. Extract into three buckets: - **Must-have** (in requirements / "required" / repeated ≥2 times / in the title) - **Nice-to-have** (preferred, "plus", "good to have") - **Context terms** (domain, methodologies, tools, soft-skill phrases the JD uses) Record the exact spelling the JD uses (e.g. "Node.js" vs "NodeJS", "CI/CD", "Power BI"). ## Step 2 — Map against the master resume For every keyword, classify: - **Present** — already in the resume with the same wording. - **Present, different wording** — the candidate has it but phrased differently (e.g. resume says "AWS Lambda functions", JD says "serverless") → rephrase to include the JD term. - **Adjacent** — related experience exists (e.g. JD wants GCP, resume has AWS) → do NOT claim the keyword; mention it in the gap report with a suggested honest line ("cloud platforms (AWS; GCP exposure)" only if the user confirms GCP exposure). - **Missing** — no evidence → list in gap report. Never add to the resume. Before writing, if any Adjacent/Missing item is a must-have, ask the user once, in a single message, whether they have that experience. Add only what they confirm. ## Step 3 — Rewrite Follow `references/ats-rules.md` strictly. In order: 1. **Headline + Summary** (3–4 lines): mirror the JD's job title if truthful, state total experience exactly as in profile (e.g. "4.11 years" → write "~5 years" only if the user approves; default "4+ years"), 4–6 must-have keywords, one quantified outcome. 2. **Skills**: grouped lines (Languages, Frameworks, Cloud/DevOps, Data, Tools — adapt to domain), JD must-haves first, JD spelling, no skill bars or ratings. 3. **Experience**: keep every role, company and date exactly as in master. Reorder and rewrite bullets so the most JD-relevant are first; each bullet = action verb + what + tech/keyword + measurable result. Keep numbers from the master; never invent metrics — if a bullet lacks one, ask or leave it qualitative. 4–6 bullets for the current role, 2–4 for older ones. 4. **Projects / Achievements / Certifications / Education**: keep every item; reorder projects so the most JD-relevant come first; keep the master's section names (e.g. Key Projects). 5. Length: keep the master's page count (1 page if the master is 1 page). ## Step 4 — Build the files Write the tailored content as JSON matching `scripts/resume_schema.md`, then run: ```bash python3 ${CLAUDE_PLUGIN_ROOT}/skills/tailor-resume/scripts/build_resume.py content.json job-hunt/output/<Name>_<Company>_<Role> ``` This creates the DOCX (single column, standard headings, Calibri 10.5–11pt, no tables/text boxes/headers/footers/images) and converts it to PDF with LibreOffice. If python-docx or LibreOffice is missing, install python-docx with pip; if PDF conversion still fails, fall back to the environment's docx and pdf skills and tell the user. ## Step 5 — Verify and report 1. Run the ATS check on the generated PDF and fix every FAIL before delivering: `python3 ${CLAUDE_PLUGIN_ROOT}/skills/tailor-resume/scripts/ats_check.py <pdf> --pages <master page count> --keywords "<JD must-haves the resume truthfully contains>"` Also run it on the master resume in Step 0. 2. Compute the keyword match score = matched must-haves ×2 + matched nice-to-haves, as a % of the maximum. Report before → after. 3. Present the match report (see `references/keyword-extraction.md` for format): score, keywords added, keywords still missing with honest suggestions (course, small project, or how to address in interview). 4. 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