cover-letter-builder

name

cover-letter-builder

description

Build a tailored cover letter from a CV or resume (PDF, plain text, Markdown, LaTeX, or a file path) matched to a job posting. Proactively asks for the user's CV if not provided, then scans the repo as a supplement. Adapts tone, length, and structure to the application channel and company culture. Outputs a polished Markdown file named YYYY-MM-DD-company-position.md. Use this skill whenever the user asks to write a cover letter, apply for a job, draft an application letter, or wants to generate a cover letter from a job posting — even if they just paste a job description, drop a PDF path, or say "write me a cover letter for this role".

Cover Letter Builder

Inputs

Field	Source
Job title	User message or job posting
Company name	User message or job posting
Job description	Pasted text, or file path to a .pdf / .txt
CV / resume	File path (.pdf, .md, .tex, .txt) or pasted text — asked in Step 0 if not provided
Application channel	Default: standard; options: linkedin
Repo path	Default: current working directory (.)

If any required field is missing and cannot be inferred, ask once — all at once.

---

Step 0: Intake

Run this before scanning the repo.

Has the user already provided a CV, resume, or background text?
  YES → proceed to Step 1 with that as the primary source
  NO  → ask the user (single message):

"Do you have an existing CV or resume? If yes, provide the file path or paste the text here. If not, I'll scan the current repo for your background."

Also confirm the job description is complete. If the user only provided a job title and company name, ask for the full JD now.

If the user has no CV and the repo scan (Step 1) finds nothing useful: switch to inline intake — ask these questions in one message:

• Current role and company
• 2-3 most relevant past roles (title, company, one key outcome each)
• Top 5-6 skills most relevant to the target role
• Education (brief)

Use the answers as the profile source.

---

Step 1: Scan Sources

If the user provided a CV file (Step 0)

Use it as the primary source. Extract text based on format:

PDF:

# Best layout preservation (poppler-utils)
pdftotext -layout cv.pdf -

Fallback chain:

1. pdftotext -layout file.pdf -
2. pdftotext file.pdf -
3. If pdftotext is unavailable: tell the user and ask them to paste the CV text

Markdown / plain text: read directly — no transformation needed.

LaTeX: strip commands, extract readable text:

sed 's/\\[a-zA-Z]*{[^}]*}//g; s/\\[a-zA-Z]*//g; s/[{}]//g'

Or pandoc -t plain file.tex if available.

Repo scan (supplementary — always runs)

Recursively scan the repo for additional context not in the CV.

Priority targets:

• *.md, *.txt, *.tex, *.rst — prose, CVs, bios, READMEs
• resume.*, cv.*, about.*, bio.*, profile.* (any extension)
• CLAUDE.md or claude.md — may contain identity/context
• *.pdf matching cv* or resume* naming — extract via pdftotext
• Any file with keywords: experience, skills, education, projects, work, career, background

Skip:

• node_modules/, .git/, vendor/, dist/, build/, *.lock, *.log, binaries

Extract from all sources:

• Name, contact info
• Work history: company, title, dates, key responsibilities
• Skills: technical, tools, languages, platforms
• Education
• Projects: name, description, tech used, outcomes
• Achievements, metrics, certifications

Merge all sources. Prefer the explicit CV as primary; use repo content to fill gaps or add project detail. Synthesize into a single internal profile. Do not write this to disk.

---

Step 2: Research the Company

Before writing, gather signals about the company's culture and communication style:

• Tone of the job posting: casual and direct (startup) vs. structured (enterprise) vs. mission-heavy (nonprofit/social impact)
• Company handbook / careers page: GitLab, Basecamp, Buffer, Automattic, and others publish their values and how they hire — read them if available
• Recent blog / engineering posts: shows what they care about technically and what language they use
• Repeated keywords in the JD: if "ownership", "shipping", "scale", "trust", or "craft" appear more than once, mirror them naturally in the letter

This research takes 2 minutes and is the difference between a generic letter and one that reads like the candidate did their homework.

---

Step 3: Analyze the Job Posting

Parse for:

• Must-haves: required skills, years of experience, specific tools
• Nice-to-haves: preferred qualifications
• Role context: team size, product area, company stage, mission
• Tone signals: startup vs enterprise, technical vs cross-functional
• Terminology to mirror: specific words they repeat

---

Step 4: Match Profile to Posting

Build a relevance map:

• Which experiences directly match the role's requirements?
• Which projects demonstrate the core skills?
• What are the strongest 2-3 signals to lead with?
• Any gaps? Bridge honestly with adjacent experience or trajectory — never fabricate.

---

Step 5: Select Channel Format

Standard (default) — full cover letter

• 4 paragraphs, 250–380 words, hard cap 400
• Structure:
  1. Opening — your strongest result or clearest signal, directly tied to the role. Do not open with "I am excited to". Lead with what you bring, not how you feel.
  2. Body 1 — match 2-3 job requirements to specific outcomes with numbers and the job's own keywords
  3. Body 2 — something specific about this company that shows you read more than the job title; connect it to why this role at this company makes sense for you
  4. Closing — one sentence of intent, one sentence CTA. No "please find attached", no "do not hesitate"

LinkedIn Easy Apply — short cover note

Use when applying through LinkedIn. LinkedIn cover notes are read on mobile, in a narrow column, and hiring managers skim them in seconds.

• 100–150 words, single block of prose, no line breaks between sentences
• Lead with your strongest match to the role
• One company-specific sentence
• End with availability and contact preference
• No letterhead, no date, no signature block
• No Markdown formatting — plain text only

---

Step 6: Write the Cover Letter

Structure (standard format)

[City, Date — optional, infer from repo if available]

[Hiring Manager name / "Hiring Team" if unknown]
[Company Name]

[Opening paragraph]

[Body paragraph 1]

[Body paragraph 2]

[Closing paragraph]

[Name]
[Contact — if found in repo]

Writing Rules (anti-AI-polish — enforce these strictly)

Do:

• Write like a sharp professional who knows their value — direct, confident, no hedging
• Use specific numbers, names, outcomes when available ("reduced P95 latency by 40%", "led migration of 3 services")
• Vary sentence length deliberately — short punches, longer explanations, not uniform rhythm
• Start sentences mid-thought when natural ("Three years building X taught me...")
• Use contractions where they read naturally ("I've", "it's", "that's")
• Reference something specific about the company from your Step 2 research
• Let personality show in 1-2 places — not every sentence

Do NOT:

• Use any of these phrases: "I am excited to", "I am passionate about", "thrilled", "leverage", "synergy", "utilize", "fast-paced environment", "team player", "detail-oriented", "results-driven", "I would be a great fit", "please find attached", "do not hesitate", "I am writing to express"
• Open with "I" as the first word of the letter
• Use the em dash (—) as a stylistic flourish more than once
• Use bullet points inside the letter body
• Summarize the job description back to the employer
• Start consecutive sentences with the same word
• Use passive voice where active works ("was responsible for" → "ran", "owned", "shipped")
• Write in lists or numbered points — prose only
• Use filler transitions: "Furthermore", "Additionally", "In conclusion", "To summarize"
• Write every sentence to the same length — ATS and human reviewers both flag uniform rhythm as a model signature
• Use abstract nouns doing verb work: "facilitation of", "enablement of", "optimization of"
• Pair adjectives in threes: "dedicated, hardworking, and results-oriented"
• Use "not only X, but also Y" constructions — a strong model tell

Tone calibration:

• Startup / early-stage: tight, direct, drop formality, show builder instinct
• Enterprise / corporate: structured, reference scale and process
• Mission-driven: acknowledge the mission briefly but don't perform enthusiasm
• Technical-first companies (Stripe, Linear, Cloudflare): precise language, show you think clearly in writing

---

Step 7: Grammar & Quality Gate

Run this pass on the full draft before writing to disk. Fix everything in-place.

Grammar checks

• Subject-verb agreement throughout
• Tense consistency — past for previous roles, present for current
• Article usage: "a" vs "an", missing articles before nouns
• Preposition correctness ("responsible for", not "responsible of")
• Comma splices — split or join with a conjunction
• Dangling modifiers — subject of opening clause must match main clause subject
• Pronoun clarity — no ambiguous "it", "they", "this" without clear referent
• Hyphenation: compound modifiers before nouns ("full-stack engineer", "five-year track record")

Sentence quality checks

• No sentence over 35 words — break it up
• No two consecutive sentences starting with the same word
• No consecutive sentences of similar length — vary rhythm
• Each paragraph has a single clear purpose; if it doesn't, rewrite or cut
• Cut words that don't earn their place: "very", "really", "quite", "just", "actually", "basically", "essentially"
• Cut throat-clearing openers: "As someone who...", "Throughout my career...", "With X years of experience..."

Authenticity re-check

• Re-scan for banned phrases from the Do NOT list — they creep back in during revision
• Verify no sentence reads like it was generated by a language model:
  • Overly balanced "not only X, but also Y" constructions
  • Suspiciously perfect parallelism across all clauses
  • Abstract nouns doing verb work ("facilitation of", "enablement of")
  • Every sentence approximately the same length
• At least one sentence must feel like this specific person wrote it — grounded in a real detail from their background

Quality bar

The letter passes when:

• A native English speaker finds nothing to correct
• It reads like a sharp professional wrote it, not like a template was filled in
• Every claim is traceable to something in the repo scan
• Cutting any paragraph would make the letter weaker
• The company-specific reference in Body 2 is concrete, not generic ("I've been following your work on X" is not concrete; citing a specific post, product decision, or value is)

If the draft does not pass, rewrite the failing sections and re-check. Do not output a letter that doesn't clear this bar.

---

Step 8: Output

Filename format

<YYYY-MM-DD>-<company-slug>-<position-slug>.md

Rules for slug:

• Lowercase, hyphens only, no special chars
• Company: acme-corp, stripe, scale-ai
• Position: senior-backend-engineer, staff-sre, founding-engineer
• Date: today's date

Example: 2026-05-03-stripe-staff-platform-engineer.md

File content format

Detect format before writing — in priority order:

1. User specified a format → use it exactly
2. Existing cover letter files found in repo → mirror structure (header style, date format, signature block)
3. No prior format found → use the default below

Default:

# Cover Letter — [Position] at [Company]

[Date]

[Body of the letter]

---
_Review before sending._

When mirroring: preserve section order, delimiter style, metadata fields — only replace body content.

Write the file to the current working directory (or repo root if specified).

---

Step 9: Report to User

After writing:

1. State the output path
2. Show the letter in full for review
3. List the top 3 repo sources used (filename → what was extracted)
4. Flag any gaps between the job requirements and what was found in the repo

---

Edge Cases

Situation	Handling
No relevant files found, no CV provided	Use inline intake (Step 0): ask current role, key experiences, top skills, education
Only a README found	Extract what's there; note it's thin; produce best-effort letter
User provides a PDF CV	Extract via pdftotext; ask user to paste text if tool unavailable
User pastes CV text directly	Parse it directly; no file extraction needed
Multiple CV/resume files	Merge; prefer the explicit CV from Step 0, then the most recent by filename
LaTeX with complex macros	Use pandoc first; fall back to regex strip
Job description is vague/short	Ask for one clarifying detail: what does "success" look like in year 1?
Company name has spaces/symbols	Slugify: Scale AI → scale-ai, Acme Corp. → acme-corp
No company handbook/public values found	Rely on tone signals from the job posting alone
LinkedIn channel selected	Output plain-text cover note (100–150 words), no Markdown, no letterhead