Agent skill

Build Tailored Resume

by SankaiAI in SankaiAI/ats-optimized-resume-agent-skill

A skill your agent uses whenever the user wants to create a tailored resume for a specific job posting.

MITAuto-check: notesBusiness, Finance & HR

Install Build Tailored Resume

skills CLI
$ npx skills add SankaiAI/ats-optimized-resume-agent-skill --skill build-tailored-resume -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install SankaiAI/ats-optimized-resume-agent-skill build-tailored-resume --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
build-tailored-resume
GitHub stars
106
Token cost
~4.4k tokens
SKILL.md length
1,677 words
Files
44
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses whenever the user wants to create a tailored resume for a specific job posting.

  • Works in 7 steps: Intake (Required) → JD Analysis (Required) → Strategy (Required) → …
  • The user wants to create a tailored resume for a specific job posting
  • SKILL.md covers Workflow design, Hard sequencing rules, Stage 1 — Intake (Required) and Stage 2 — JD Analysis (Required), plus 9 more sections
  • Runs Batch, PowerShell and Shell scripts from its folder; calls python and pip; reaches github.com and linkedin.com

What it does

Build Tailored Resume is an agent skill from SankaiAI/ats-optimized-resume-agent-skill. Use this skill whenever the user wants to create a tailored resume for a specific job posting. Triggers: 'tailor my resume', 'write me a resume for this job', 'customize resume for JD', 'build a targeted resume', 'resume for [company]', or any request matching a candidate background to a job description and producing a .docx. Also triggers when the user provides a master resume alongside a job posting. Do NOT use for general resume advice or cover letters.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 48 other files (for example `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json` and `PRIVACY.md`).

It sits in Business, Finance & HR, covering Resume and CV writing and Word documents. It works with Microsoft Word, LinkedIn and GitHub. The repository describes itself as: This is an agent skill for coding agents like Claude code to use to tailor your resume, avoiding AI-generated wordings and automatically generate a concise and completely… The licence is MIT.

When your agent uses it

  • The user wants to create a tailored resume for a specific job posting
  • The user provides a master resume alongside a job posting
  • General resume advice

Example prompts

  • “tailor my resume”
  • “write me a resume for this job”
  • “customize resume for JD”
  • “/build-tailored-resume”

Requirements

  • Python 3
  • A Bash shell
  • PowerShell
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Intake (Required)
  2. JD Analysis (Required)
  3. Strategy (Required)
  4. Content Tailoring (Required)
  5. ATS Check (Required)
  6. Render (Required)
  7. Final Validation (Required)

What it can do on your machine

Read from SKILL.md and the folder at commit 153209b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep
    • WebSearch
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Batch, PowerShell and Shell, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com
    • linkedin.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Build Tailored Resume loads about 4.4k tokens when it runs. Until then it costs about 121 tokens; SKILL.md has 1,677 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from SankaiAI/ats-optimized-resume-agent-skill at commit 153209b, republished under its MIT licence (© SankaiAI). 1,677 words, ~4,441 tokens.

Download SKILL.mdSave it as .claude/skills/build-tailored-resume/SKILL.md (or your agent's skills folder). This skill also uses 43 other files; get the full folder from GitHub.
name
build-tailored-resume
description
Use this skill whenever the user wants to create a tailored resume for a specific job posting. Triggers: 'tailor my resume', 'write me a resume for this job', 'customize resume for JD', 'build a targeted resume', 'resume for [company]', or any request matching a candidate background to a job description and producing a .docx. Also triggers when the user provides a master resume alongside a job posting. Do NOT use for general resume advice or cover letters.
allowed-tools
Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch
argument-hint
[optional: output filename]
license
MIT

build-tailored-resume

You are an expert resume strategist, ATS optimization specialist, and document generation engineer.

Your job is to take a user's master resume (or raw experience inventory) and a target job description, then produce:

  1. A tailored, role-specific resume with human-sounding bullets
  2. A polished Word document (.docx) via deterministic Python rendering

Workflow design

This skill uses a guardrailed workflow, not rigid choreography.

  • Required stages always run — they cannot be skipped
  • Optional stages run only when conditions warrant
  • Gates are hard stops between stages — you cannot advance past a gate until its conditions are met
  • Flexibility lives within stages, not in skipping them
[INTAKE] ──GATE 1──> [JD ANALYSIS] ──GATE 2──> [STRATEGY] ──GATE 3──> [CONTENT TAILORING]
                                                     ^                          |
                                             [OPT: company research]    [humanization pass]
                                             [OPT: team inference]             |
                                                                        ──GATE 4──> [ATS CHECK]
                                                                                        |
                                                                                ──GATE 5──> [RENDER]
                                                                                                |
                                                                                        ──GATE 6──> [VALIDATE]

Announce each stage before starting it:

=== [Stage Name] ===

Adapt your depth per stage based on how much the user has already provided. If inputs are complete and explicit, move fast. If inputs are messy or incomplete, do more work.


Hard sequencing rules

These cannot be bypassed regardless of how complete the input is:

  • No drafting before JD analysis is complete (Gate 2 must pass)
  • No ATS check before bullet selection and rewriting (Gate 4 must pass)
  • No rendering before content validation (Gate 4 must pass)
  • No final DOCX before JSON schema validation (Gate 5 must pass)
  • No final output before humanization pass (Gate 4 must pass)

Stage 1 — Intake (Required)

Collect and normalize all inputs.

Required inputs:

  • Candidate full name, email, phone
  • Master resume or experience inventory (any format: old resume, raw text, LinkedIn export, brag doc)
  • Target job description
  • Target company name

Optional inputs (infer or skip if not provided):

  • LinkedIn, GitHub, portfolio URLs — actively scan the source resume text for these; look for linkedin.com/in/... and github.com/... patterns. The urls could be embeded in the words and you will need to extract the urls from them. If the resume only shows the word "LinkedIn" or "GitHub" without a URL, note this gap and flag it in Stage 7 rather than leaving the field empty.
  • Career level preference (new_grad / entry_level / mid_level / senior_ic / manager / director / auto)
  • Output length preference (one_page / two_page / auto)
  • Tone preference (conservative / modern_professional / technical / analytical)
  • Location (omit if privacy preferred)
  • Roles or projects to emphasize or downplay
  • Specific metrics the user can confidently defend

Flexibility: Ask only what is blocking. If master resume + JD + company are provided, move directly to Gate 1 without asking anything. If input is messy or missing key fields, ask concise targeted questions — at most 3 at a time.

GATE 1

Before advancing, verify:

  • Candidate name, email, phone are known or inferable
  • Master resume or experience inventory exists and is readable
  • Target job description is available
  • Target company name is known

If any gate condition fails, ask the user for the missing input before proceeding.


Stage 2 — JD Analysis (Required)

Extract from the job description:

  • Required vs. preferred qualifications (distinguish clearly)
  • Repeated skills and action verbs — these are high-signal ATS keywords
  • Likely business KPIs and domain language
  • Seniority clues (IC vs. manager, scope of ownership, leadership expectations)
  • Business function: product / marketing / growth / ops / finance / data / platform / research / engineering
  • Likely ATS screening terms

Output a brief summary of what this role prioritizes — 3-5 bullet points. This drives everything downstream.

GATE 2

Before advancing, verify:

  • Required qualifications are extracted
  • Top 5-8 ATS keywords are identified
  • Role seniority level is determined
  • Business function and domain are identified

Stage 2a — Company Research (Optional)

Run this stage when: company context is not already obvious from the JD, or when the company name is unfamiliar.

Skip when: the JD already contains extensive company context, or the company is a household name whose business model is widely known.

Use WebSearch: "[company name]" business model products customers

Identify:

  • Business model (B2B SaaS, eCommerce, marketplace, adtech, healthcare, staffing, fintech, etc.)
  • Core products or services
  • Likely business KPIs and priority language
  • Company size / stage if visible

Domain language mapping — use to align candidate language to company language:

DomainKey language
B2B SaaSproduct adoption, ARR, churn, activation, time-to-value, seat expansion
eCommerceconversion, ROAS, CAC, LTV, funnel, merchandising, cart abandonment
Healthcare / insurancecompliance, accuracy, turnaround, cost containment, prior auth
Marketplacesupply-demand balance, pricing, merchant health, fulfillment, take rate
Adtechincrementality, attribution, campaign performance, clean room, reach
Staffing / HR techplacement rate, time-to-fill, candidate quality, client retention
Fintechpayment volume, fraud rate, underwriting, activation, regulatory

Stage 2b — Team Inference (Optional)

Run this when: the JD hints at team structure (e.g., "join a team of X", "work with product and engineering", "report to VP of...") and that context would affect how the candidate frames their experience.

Infer:

  • Who the candidate would report to and collaborate with
  • What cross-functional influence the role requires
  • Whether individual contributor or collaborative execution is emphasized

Stage 3 — Strategy (Required)

Before writing a single bullet, define the resume's strategy in writing:

  1. Top 3 strengths to lead with — why this candidate fits this role
  2. 1-2 areas to downplay — background that is irrelevant or potentially distracting
  3. Page 1 headline story — what a recruiter sees in 10 seconds
  4. Section order — based on candidate level:
LevelSection order
new_grad / internEducation → Experience → Projects → Skills → Awards
entry_levelExperience → Skills → Education → Projects
mid_level / senior_icSummary → Skills(Optional if decided to mentioned the skills in the experience directly to make all content on single page to save space) → Experience → Projects → Education
manager / directorExecutive Summary → Experience → Education → Boards/Certs
  1. Sections to include or omit:

    • Summary: include for mid+ level; optional for entry; omit for new_grad
    • Projects: include if they add material signal not covered by experience
    • Certifications: include only if role-relevant
    • Awards: include only if senior or prestigious
  2. Page target: one_page or two_page — decide based on experience depth and role seniority, not user preference alone

GATE 3

Before advancing, verify:

  • Strategy is written out (not just decided internally)
  • Section order and page target are set
  • At least 3 specific strengths are identified that link candidate experience to JD requirements

Stage 4 — Content Tailoring (Required)

4a — Bullet selection

From the master resume:

  • Select bullets based on target fit, not recency
  • Remove good-but-irrelevant bullets
  • Prioritize: role-relevant keywords, concrete metrics, ownership signals, scope signals
  • Keep 3-5 bullets per recent/relevant role; 2-3 for older or less relevant roles
  • Match total bullet count to page target
Show full SKILL.md (661 more words)Show less
4b — Bullet rewriting

Rewrite selected bullets in human-professional style.

Strong bullet structure:

Action verb → what was built/analyzed/driven → method or tool → measurable result → scale or context

Example:

"Built SQL and Tableau dashboards tracking campaign KPIs, cutting weekly reporting time by 90% across 12 stakeholders."

Rules:

  • Use concrete verbs tied to real work: built, analyzed, shipped, reduced, negotiated, automated, redesigned
  • Use specific nouns (pipeline names, tool names, team sizes, dollar amounts)
  • Vary sentence rhythm — avoid starting 3+ consecutive bullets the same way
  • Never: "responsible for", "helped with", "assisted in", "supported"
  • Never: "results-driven", "dynamic", "passionate", "visionary", "innovative"
  • Never: generic AI summary language ("proven track record of delivering value...")
  • Every bullet must pass: "Could this candidate explain it naturally in 30 seconds?"
  • Every metric must pass: "Would a hiring manager believe this at this seniority level?"

If no metric exists, use a concrete dimension instead:

  • Scale: number of users, tables, pipelines, markets
  • Speed: latency, turnaround time, frequency
  • Accuracy: error rate, model precision, test coverage
  • Adoption: team count, stakeholder count, integration count
  • Throughput: volume processed, coverage rate
4c — Humanization pass (Required)

Run this pass over all written content. Print results:

Humanization check:
[PASS] No vague buzzwords
[PASS] No repeated sentence syntax across 3+ consecutive bullets
[PASS] No suspicious over-optimization
[PASS] No generic AI summary patterns
[PASS] All bullets sound like a real person wrote them

If any check fails, rewrite the offending bullets before proceeding.

GATE 4

Before advancing, verify:

  • All bullets are selected and rewritten
  • Humanization pass is complete with all checks passing
  • No unsupported claims (invented metrics, tools, titles, or projects the user never mentioned)
  • Content fits the page target

Stage 5 — ATS Check (Required)

Run after bullet selection and rewriting. Cannot run before Gate 4.

Print results:

ATS check:
[PASS] Section headings are standard (Experience, Education, Skills, Projects, Certifications)
[PASS] Top JD keywords appear naturally — matched: SQL, Tableau, A/B testing, cohort analysis, ETL
[PASS] Contact info is clean and parse-friendly
[PASS] No keyword stuffing
[PASS] No decorative symbols or icons in body text
[PASS] Dates and titles are consistent

If any check fails, fix before proceeding.

GATE 5

Before advancing, verify:

  • All ATS checks pass
  • At least 5 JD keywords are present naturally in the content

Stage 6 — Render (Required)

Generate the structured resume JSON, then call the renderer.

JSON schema
json
{
  "candidate_level": "senior_ic",
  "target_role": "Senior Data Analyst",
  "target_company": "Stripe",
  "section_order": ["header", "summary", "skills", "experience", "projects", "education"],
  "header": {
    "name": "Alex Chen",
    "email": "alex.chen@email.com",
    "phone": "(415) 555-0192",
    "location": "San Francisco, CA",
    "linkedin": "https://linkedin.com/in/alexchen",
    "github": "https://github.com/alexchen",
    "website": ""
  },
  "summary": "Data analyst with 5 years building SQL pipelines, product analytics, and experimentation frameworks at fintech and SaaS companies. Strong track record shipping self-serve dashboards that reduce reporting overhead and improve decision speed for product and growth teams.",
  "skills": {
    "core": ["SQL", "Python", "Tableau", "dbt", "A/B Testing"],
    "tools": ["BigQuery", "Snowflake", "Looker", "Airflow", "Statsig"],
    "methods": ["Cohort Analysis", "Funnel Analysis", "Experiment Design", "Regression"],
    "domains": ["Product Analytics", "Growth", "Payments", "Fintech"]
  },
  "experience": [
    {
      "title": "Senior Data Analyst",
      "company": "Brex",
      "location": "San Francisco, CA",
      "dates": "Jan 2022 – Present",
      "bullets": [
        "Built SQL + dbt pipelines tracking spend adoption across 2,400 SMB customers, surfacing segment cohorts that informed a product roadmap shift adopted by 3 PMs.",
        "Designed A/B test framework in Statsig for card onboarding flow, improving 30-day activation rate by 14% and reducing time-to-first-spend by 4 days.",
        "Automated 6 weekly finance reports via Airflow + BigQuery, saving 8 hours/week of analyst time."
      ],
      "links": []
    }
  ],
  "projects": [
    {
      "name": "Open Source SQL Query Optimizer",
      "subtitle": "Personal project",
      "location": "",
      "dates": "2023",
      "bullets": [
        "Built a Python tool that analyzes BigQuery query plans and suggests index and partitioning improvements; 340 GitHub stars."
      ],
      "url": "https://github.com/alexchen/sql-optimizer"
    }
  ],
  "education": [
    {
      "school": "UC San Diego",
      "location": "San Diego, CA",
      "dates": "2017 – 2021",
      "degree": "B.S. Cognitive Science, Data Science emphasis",
      "details": []
    }
  ],
  "certifications": [],
  "awards": [],
  "metadata": {
    "page_target": "one_page",
    "tone": "modern_professional",
    "ats_focus": true,
    "humanization_pass_complete": true
  }
}
Render command

CRITICAL: Always use the official renderer. Never write an ad-hoc renderer script — doing so bypasses all formatting guarantees (two-column date/location layout, hyperlinks, name heading spacing) and will produce a broken document.

Step 1 — confirm the CLI is reachable:

bash
resume-skill --version 2>/dev/null || echo "NOT_INSTALLED"

Step 2 — if the output was NOT_INSTALLED, install via pip (package was installed at skill setup time; this just re-registers it):

bash
pip install resume-skill --quiet 2>/dev/null || python -m pip install resume-skill --quiet

Step 3 — render using whichever invocation works:

bash
# Primary (CLI on PATH):
resume-skill render --input tailored_resume.json --output tailored_resume.docx

# Fallback (module invocation — works even when PATH doesn't include Python Scripts):
python -m resume_skill.cli render --input tailored_resume.json --output tailored_resume.docx

If neither works, diagnose the pip install error — do NOT fall back to writing a new renderer.

Validate only (no DOCX):

bash
resume-skill validate --input tailored_resume.json
# or
python -m resume_skill.cli validate --input tailored_resume.json
GATE 6

Before declaring done, verify:

  • JSON schema validation passes (resume-skill validate reports PASS)
  • DOCX was written without errors — if any render error occurs, diagnose and fix it; do not fall back to writing a new renderer
  • Output path is confirmed and shown to the user

Stage 7 — Final Validation (Required)

After rendering, confirm:

  • No missing required header fields
  • No placeholder text remaining (e.g. "YOUR NAME", "TODO")
  • Section order matches strategy decided in Stage 3
  • Bullet counts fit page target
  • No duplicate bullets across roles

Then deliver to the user:

  • Full path to the .docx file
  • Brief strategy summary: what was emphasized, what was downplayed, ATS keywords woven in
  • Any honest gaps (missing tools, unsupported claims flagged, things to verify)

What NOT to do

  • Do not invent tools, metrics, projects, or leadership the user never mentioned
  • Do not keyword stuff
  • Do not use generic AI summaries ("passionate professional who thrives in dynamic environments")
  • Do not require the user to manually fix Word formatting
  • Do not ask more than 3 clarifying questions at a time
  • Do not dump generic resume advice without producing the actual tailored output
  • Do not skip a required stage even if the input looks complete

Customizing the renderer

All layout settings are in src/rendering/config.py:

SettingControls
font_nameBody and contact font family
name_font_sizeCandidate name size (pt)
body_font_sizeBullet and body text size (pt)
section_spacing_beforeSpace above each section header (pt)
show_section_bottom_borderToggle section divider lines
section_border_colorHex color of section divider lines
margin_*_inchesPage margins
section_orderDefault section ordering
link_colorHyperlink hex color

Override any value by passing a modified RenderConfig to ResumeRenderer, or set metadata.section_order in the resume JSON to control section ordering per resume.

© SankaiAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 43 other files in the repository root of SankaiAI/ats-optimized-resume-agent-skill.

  • SKILL.md
  • .claude-plugin/marketplace.json
  • .claude-plugin/plugin.json
  • .gitignore
  • LICENSE
  • PRIVACY.md
  • README.md
  • bin/resume-skill
  • bin/resume-skill.cmd
  • install.ps1
  • install.sh
  • renderer/examples/output/alex_chen_no_skills.docx
  • renderer/examples/output/alex_chen_stripe.docx
  • renderer/examples/output/alex_chen_stripe_v2.docx
  • renderer/examples/output/install_test.docx
  • renderer/examples/output/manual_install_test.docx
  • … and 28 more

Open the folder on GitHubat commit 153209b

Compare with similar skills

Build Tailored Resume next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Build Tailored Resume compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Build Tailored Resume this skillSankaiAI/ats-optimized-resume-agent-skill106—~4.4kAutomated safety check: NotesMIT
Resume BuilderJichengyuuuuu/resume-builder-skill164—~1.5kAutomated safety check: PassNone
Lockedindaypunk/LockedIn128—~3.2kAutomated safety check: PassMIT
Cell Submissionyrui-cmd/Cell110—~1.7kAutomated safety check: PassMIT
Thu ThesisLeoYeAI/openclaw-master-skills2.2k—~4kAutomated safety check: PassMIT
Release Notes Generatoramplitude/builder-skills159—~684Automated safety check: PassNone

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Questions about Build Tailored Resume

What does Build Tailored Resume do?

A skill your agent uses whenever the user wants to create a tailored resume for a specific job posting. Build Tailored Resume is an agent skill from SankaiAI/ats-optimized-resume-agent-skill. Use this skill whenever the user wants to create a tailored resume for a specific job posting.

When should I use Build Tailored Resume?

Build Tailored Resume fits situations like: the user wants to create a tailored resume for a specific job posting; the user provides a master resume alongside a job posting; general resume advice.

How do I install Build Tailored Resume in Claude Code?

Run `npx skills add SankaiAI/ats-optimized-resume-agent-skill --skill build-tailored-resume -a claude-code`. Or copy the skill folder (the SankaiAI/ats-optimized-resume-agent-skill repository) into .claude/skills/build-tailored-resume in your project. Claude Code loads it when a task matches its description.

How do I install Build Tailored Resume in Codex?

Run `npx skills add SankaiAI/ats-optimized-resume-agent-skill --skill build-tailored-resume -a codex`. Or copy the skill folder (the SankaiAI/ats-optimized-resume-agent-skill repository) into .agents/skills/build-tailored-resume in your project. Codex loads it when a task matches its description.

Can I use Build Tailored Resume in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add SankaiAI/ats-optimized-resume-agent-skill --skill build-tailored-resume -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/build-tailored-resume, .gemini/skills/build-tailored-resume, .github/skills/build-tailored-resume and .opencode/skills/build-tailored-resume in your project.

What does Build Tailored Resume need to run?

Going by SKILL.md and its folder, Build Tailored Resume needs Windows cmd, PowerShell and a shell for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3; A Bash shell; PowerShell. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch, WebFetch.

Does Build Tailored Resume access the network?

SKILL.md names 2 domains. In commands or code: github.com and linkedin.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Build Tailored Resume safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Build Tailored Resume use?

Build Tailored Resume is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Build Tailored Resume use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Build Tailored Resume?

Skills that share tags, products or a category with Build Tailored Resume: Resume Builder (Jichengyuuuuu/resume-builder-skill, 164 stars), Lockedin (daypunk/LockedIn, 128 stars), Cell Submission (yrui-cmd/Cell, 110 stars) and Thu Thesis (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Build Tailored Resume?

SankaiAI (a GitHub user) maintains it in SankaiAI/ats-optimized-resume-agent-skill, which has 106 GitHub stars. The repository was last updated on April 11, 2026.

Source: SankaiAI/ats-optimized-resume-agent-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.