Vibecode CVMatchScore
track this build5 steps, step by step0%KINDA · weekend project
The core loop, resume plus job posting into an LLM holding a scoring rubric, is one prompt and an afternoon, and for improving one resume against one posting it genuinely works. The honest gap is calibration: a rubric you wrote today measures today's mood, two runs of the same resume can disagree, and a 72 means nothing without a baseline of scored applications behind it. Fine as a mirror, thin as a measuring stick.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of CVMatchScore. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # CVMatchScore indie build ## Goal Build the smallest trustworthy replacement for the core CVMatchScore workflow for one developer or a tiny team. ## Scope Extract text from the resume PDF, send it with the job description to an LLM holding a fixed scoring rubric, get structured JSON scores per criterion with quoted evidence, render a Markdown report. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - a calibrated rubric that scores the same resume the same way twice - 50+ language support tested per parameter - DOC, DOCX, and RTF parsing beyond PDF - improvement plans and tailored cover letters built from the same analysis - scores comparable across weeks of applications If those capabilities are essential, use pdfplumber instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a local resume match scorer to replace CVMatchScore. Requirements: - A Node 22 CLI: `match score resume.pdf job.txt` prints a score table and writes a Markdown report to reports/YYYY-MM-DD-<company>.md. - Extract resume text with pdf-parse; accept .txt and .md for the job posting. - Keep the rubric in rubric.json: 10 criteria (skills overlap, seniority fit, domain experience, quantified achievements, education, keyword coverage, employment gaps, clarity, length, ATS-safety), each with a 0-10 definition and a weight. - One Anthropic structured-outputs call scores all criteria at once and must quote the resume line that justifies each score, no unquoted claims. - A second cheap pass lists the 10 most important posting keywords missing from the resume and where each could honestly fit. - Weighted total out of 100, computed in code from rubric.json, not by the model. - Store every run in SQLite via better-sqlite3: date, company, total, and the per-criterion JSON, so `match history` shows my scores over time. - API key from .env. No accounts, no telemetry, the resume never leaves my machine except the API call. - Out of scope: cover letter generation, DOC/DOCX parsing, multi-language support, and recruiter-style bulk ranking. - README: setup, cost per run, and a warning that scores are only comparable within one rubric version, so bump a version field in rubric.json when I edit it. ## Required capabilities - OpenAI or Anthropic API key - pdf-parse or pdfplumber for resume text extraction - a written rubric with a 0-10 definition per criterion - a few real resume and posting pairs to sanity-check the scores ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of CVMatchScore. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # CVMatchScore indie build ## Goal Build the smallest trustworthy replacement for the core CVMatchScore workflow for one developer or a tiny team. ## Scope Extract text from the resume PDF, send it with the job description to an LLM holding a fixed scoring rubric, get structured JSON scores per criterion with quoted evidence, render a Markdown report. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - a calibrated rubric that scores the same resume the same way twice - 50+ language support tested per parameter - DOC, DOCX, and RTF parsing beyond PDF - improvement plans and tailored cover letters built from the same analysis - scores comparable across weeks of applications If those capabilities are essential, use pdfplumber instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me a local resume match scorer to replace CVMatchScore. Requirements: - A Node 22 CLI: `match score resume.pdf job.txt` prints a score table and writes a Markdown report to reports/YYYY-MM-DD-<company>.md. - Extract resume text with pdf-parse; accept .txt and .md for the job posting. - Keep the rubric in rubric.json: 10 criteria (skills overlap, seniority fit, domain experience, quantified achievements, education, keyword coverage, employment gaps, clarity, length, ATS-safety), each with a 0-10 definition and a weight. - One Anthropic structured-outputs call scores all criteria at once and must quote the resume line that justifies each score, no unquoted claims. - A second cheap pass lists the 10 most important posting keywords missing from the resume and where each could honestly fit. - Weighted total out of 100, computed in code from rubric.json, not by the model. - Store every run in SQLite via better-sqlite3: date, company, total, and the per-criterion JSON, so `match history` shows my scores over time. - API key from .env. No accounts, no telemetry, the resume never leaves my machine except the API call. - Out of scope: cover letter generation, DOC/DOCX parsing, multi-language support, and recruiter-style bulk ranking. - README: setup, cost per run, and a warning that scores are only comparable within one rubric version, so bump a version field in rubric.json when I edit it. ## Required capabilities - OpenAI or Anthropic API key - pdf-parse or pdfplumber for resume text extraction - a written rubric with a 0-10 definition per criterion - a few real resume and posting pairs to sanity-check the scores ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of CVMatchScore. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # CVMatchScore product brief ## Problem The core loop, resume plus job posting into an LLM holding a scoring rubric, is one prompt and an afternoon, and for improving one resume against one posting it genuinely works. The honest gap is calibration: a rubric you wrote today measures today's mood, two runs of the same resume can disagree, and a 72 means nothing without a baseline of scored applications behind it. Fine as a mirror, thin as a measuring stick. ## Product outcome Extract text from the resume PDF, send it with the job description to an LLM holding a fixed scoring rubric, get structured JSON scores per criterion with quoted evidence, render a Markdown report. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI or Anthropic API key - pdf-parse or pdfplumber for resume text extraction - a written rubric with a 0-10 definition per criterion - a few real resume and posting pairs to sanity-check the scores ## Explicit non-goals for v1 - a calibrated rubric that scores the same resume the same way twice - 50+ language support tested per parameter - DOC, DOCX, and RTF parsing beyond PDF - improvement plans and tailored cover letters built from the same analysis - scores comparable across weeks of applications ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build me a local resume match scorer to replace CVMatchScore. Requirements: - A Node 22 CLI: `match score resume.pdf job.txt` prints a score table and writes a Markdown report to reports/YYYY-MM-DD-<company>.md. - Extract resume text with pdf-parse; accept .txt and .md for the job posting. - Keep the rubric in rubric.json: 10 criteria (skills overlap, seniority fit, domain experience, quantified achievements, education, keyword coverage, employment gaps, clarity, length, ATS-safety), each with a 0-10 definition and a weight. - One Anthropic structured-outputs call scores all criteria at once and must quote the resume line that justifies each score, no unquoted claims. - A second cheap pass lists the 10 most important posting keywords missing from the resume and where each could honestly fit. - Weighted total out of 100, computed in code from rubric.json, not by the model. - Store every run in SQLite via better-sqlite3: date, company, total, and the per-criterion JSON, so `match history` shows my scores over time. - API key from .env. No accounts, no telemetry, the resume never leaves my machine except the API call. - Out of scope: cover letter generation, DOC/DOCX parsing, multi-language support, and recruiter-style bulk ranking. - README: setup, cost per run, and a warning that scores are only comparable within one rubric version, so bump a version field in rubric.json when I edit it. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted CVMatchScore capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# CVMatchScore indie build ## Goal Build the smallest trustworthy replacement for the core CVMatchScore workflow for one developer or a tiny team. ## Scope Extract text from the resume PDF, send it with the job description to an LLM holding a fixed scoring rubric, get structured JSON scores per criterion with quoted evidence, render a Markdown report. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - a calibrated rubric that scores the same resume the same way twice - 50+ language support tested per parameter - DOC, DOCX, and RTF parsing beyond PDF - improvement plans and tailored cover letters built from the same analysis - scores comparable across weeks of applications If those capabilities are essential, use pdfplumber instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build me a local resume match scorer to replace CVMatchScore. Requirements: - A Node 22 CLI: `match score resume.pdf job.txt` prints a score table and writes a Markdown report to reports/YYYY-MM-DD-<company>.md. - Extract resume text with pdf-parse; accept .txt and .md for the job posting. - Keep the rubric in rubric.json: 10 criteria (skills overlap, seniority fit, domain experience, quantified achievements, education, keyword coverage, employment gaps, clarity, length, ATS-safety), each with a 0-10 definition and a weight. - One Anthropic structured-outputs call scores all criteria at once and must quote the resume line that justifies each score, no unquoted claims. - A second cheap pass lists the 10 most important posting keywords missing from the resume and where each could honestly fit. - Weighted total out of 100, computed in code from rubric.json, not by the model. - Store every run in SQLite via better-sqlite3: date, company, total, and the per-criterion JSON, so `match history` shows my scores over time. - API key from .env. No accounts, no telemetry, the resume never leaves my machine except the API call. - Out of scope: cover letter generation, DOC/DOCX parsing, multi-language support, and recruiter-style bulk ranking. - README: setup, cost per run, and a warning that scores are only comparable within one rubric version, so bump a version field in rubric.json when I edit it. ## Required capabilities - OpenAI or Anthropic API key - pdf-parse or pdfplumber for resume text extraction - a written rubric with a 0-10 definition per criterion - a few real resume and posting pairs to sanity-check the scores ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# CVMatchScore product brief ## Problem The core loop, resume plus job posting into an LLM holding a scoring rubric, is one prompt and an afternoon, and for improving one resume against one posting it genuinely works. The honest gap is calibration: a rubric you wrote today measures today's mood, two runs of the same resume can disagree, and a 72 means nothing without a baseline of scored applications behind it. Fine as a mirror, thin as a measuring stick. ## Product outcome Extract text from the resume PDF, send it with the job description to an LLM holding a fixed scoring rubric, get structured JSON scores per criterion with quoted evidence, render a Markdown report. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - OpenAI or Anthropic API key - pdf-parse or pdfplumber for resume text extraction - a written rubric with a 0-10 definition per criterion - a few real resume and posting pairs to sanity-check the scores ## Explicit non-goals for v1 - a calibrated rubric that scores the same resume the same way twice - 50+ language support tested per parameter - DOC, DOCX, and RTF parsing beyond PDF - improvement plans and tailored cover letters built from the same analysis - scores comparable across weeks of applications ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build me a local resume match scorer to replace CVMatchScore. Requirements: - A Node 22 CLI: `match score resume.pdf job.txt` prints a score table and writes a Markdown report to reports/YYYY-MM-DD-<company>.md. - Extract resume text with pdf-parse; accept .txt and .md for the job posting. - Keep the rubric in rubric.json: 10 criteria (skills overlap, seniority fit, domain experience, quantified achievements, education, keyword coverage, employment gaps, clarity, length, ATS-safety), each with a 0-10 definition and a weight. - One Anthropic structured-outputs call scores all criteria at once and must quote the resume line that justifies each score, no unquoted claims. - A second cheap pass lists the 10 most important posting keywords missing from the resume and where each could honestly fit. - Weighted total out of 100, computed in code from rubric.json, not by the model. - Store every run in SQLite via better-sqlite3: date, company, total, and the per-criterion JSON, so `match history` shows my scores over time. - API key from .env. No accounts, no telemetry, the resume never leaves my machine except the API call. - Out of scope: cover letter generation, DOC/DOCX parsing, multi-language support, and recruiter-style bulk ranking. - README: setup, cost per run, and a warning that scores are only comparable within one rubric version, so bump a version field in rubric.json when I edit it. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted CVMatchScore capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent
At 49 USD a year it is priced below the hassle of maintaining your own: job seekers pay for stable scores they can track across applications, cover letters generated from the same pass, and not burning API credits mid job hunt.
xa calibrated rubric that scores the same resume the same way twice
x50+ language support tested per parameter
xDOC, DOCX, and RTF parsing beyond PDF
ximprovement plans and tailored cover letters built from the same analysis
xscores comparable across weeks of applications
share on X ↗"I just replaced CVMatchScore ($4.08/mo) with one prompt"
Don't feel like building it? These folks already made it free.
Resume-MatcherA local resume-vs-posting matcher that runs against Ollama, so the scoring stays on your machine; you install it, it does the job, and nobody bills you.open source↗no votes, no pay-to-list · just what's real
CVMatchScore pricing
pro$4.08/mo · yearly, converted to monthly · $48.96/yr
free tier3 full reports in the first 7 days, all 19 parameters, no credit card.
verified 2026-08-10 · source ↗
Is CVMatchScore free?
3 full reports in the first 7 days, all 19 parameters, no credit card. Paid is PRO at $4.08/mo (checked 2026-08-10).
Vibecode CVMatchScore
Kinda. The core of CVMatchScore is buildable in a weekend with the prompt on this page, but there are real gaps: a calibrated rubric that scores the same resume the same way twice, 50+ language support tested per parameter. Read the honest list above before committing.
How much does CVMatchScore cost?
CVMatchScore costs about $4.08/month (PRO, checked 2026-08-10), which is $48.96 per year.
What do I lose by replacing CVMatchScore?
Honestly: a calibrated rubric that scores the same resume the same way twice; 50+ language support tested per parameter; DOC, DOCX, and RTF parsing beyond PDF; improvement plans and tailored cover letters built from the same analysis; scores comparable across weeks of applications. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to CVMatchScore?
Yes: Resume-Matcher (A local resume-vs-posting matcher that runs against Ollama, so the scoring stays on your machine; you install it, it does the job, and nobody bills you.) The prompt is for when you want it exactly your way.