linkright 0.3.0

Local-first, agent-native career OS — resume tailoring, interview prep, job search, content planning. Runs $0 with free-tier LLM keys.

LinkRight

Local-first, agent-native career OS. Four pillars — resume, job search, interview, content — exposed as a single CLI plus an MCP server your agent can drive for ₹0.

Runs on your machine. Your data, your LLM keys, your rules.

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What is LinkRight

LinkRight is a Python CLI (linkright) that tailors resumes, evaluates job descriptions, preps interviews, and drafts social content. It runs in two modes:

• Agent mode (₹0) — Claude Code / Cursor / Gemini CLI auto-discover .claude/skills/*.md and spawn linkright mcp serve on demand. The agent does the reasoning; LinkRight provides the tools and data layer. Zero API cost for the user.
• Direct mode — LinkRight calls LLMs itself via a cascade: Groq → Gemini Flash Lite → Cerebras → OpenRouter, with Oracle-hosted gemma3:1b as the local fallback.

Data lives in a local MongoDB (linkright database, 12 collections) and ~/.linkright/.

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Install

Prereqs

• Python 3.9+ (3.13 tested, 3.11+ recommended)
• Node.js 18+ (optional but recommended — unpdf parser uses it)
• MongoDB 8 Community Edition (optional — only some flows touch it)

Recommended: pipx (isolated venv, industry standard)

brew install pipx                            # macOS — or: python3 -m pip install --user pipx
pipx ensurepath
pipx install 'linkright[full]'               # all Python deps in one shot
linkright setup                              # interactive wizard — picks LLM/embedder/PDF, downloads chromium binary
linkright doctor                             # 9-check health verify

Alternative: pip (system-wide / venv)

pip install 'linkright[full]'                # core + fastembed + playwright
linkright setup

Minimal install (advanced — for MCP-only / CI users)

pip install linkright                        # 12 core deps only, ~50MB

Then opt into extras as needed:

• pip install 'linkright[embed]' — adds fastembed (~80MB)
• pip install 'linkright[pdf]' — adds playwright (run playwright install chromium after, ~200MB)
• pip install 'linkright[weasy]' — pure-Python PDF renderer, no chromium
• pip install 'linkright[all]' — everything (full + weasy)

Source install (for development)

git clone https://github.com/satvik-jain-iitd/linkright_production.git
cd linkright_production/context/cli/linkright
pip install -e '.[full,dev]'                 # editable + extras + dev tools

Optional — MongoDB (only for DB-backed flows)

brew tap mongodb/brew
brew install mongodb-community@8
brew services start mongodb-community@8
linkright init                               # bootstrap ~/.linkright/ + Mongo collections

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Quick start

# 1. Install + bootstrap (one time)
brew install mongodb-community@8
brew services start mongodb-community@8
pip install -e .
linkright init

# 2a. Agent mode — zero cost, recommended
# Open this repo in Claude Code or Cursor. Then say:
#   "Use LinkRight to tailor my resume for this JD: <paste>"
# The agent discovers .claude/skills/tailor-resume.md and spawns `linkright mcp serve`.

# 2b. Direct mode — uses your API keys
export GEMINI_API_KEY_1=...      # primary
export GROQ_API_KEY=...           # cascade step 1
linkright resume tailor -r resume.pdf -j jds/noon.md --llm-mode direct

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Pillar 2 — Job search (v0.2.0)

Connect your sync.linkright.in job feed directly to the CLI. Browse, filter, and act on scraped + scored job matches without leaving the terminal.

Quick start

# 1. Log in once (stores JWT locally in ~/.linkright/session.json)
linkright auth login                    # prompts email + password

# 2. Browse today's top matches
linkright jobs find                     # top 10 by fit score
linkright jobs find --top 20            # see more
linkright jobs find --grade A           # A-grade only
linkright jobs find --location bangalore

# 3. Read a full JD
linkright jobs show 1                   # rank 1 from 'find' output
linkright jobs show <uuid>              # or paste the discovery ID

# 4. Tailor resume + mark applied (runs Pillar 1 pipeline automatically)
linkright jobs apply 1

# 5. Save / dismiss jobs
linkright jobs status 1 saved
linkright jobs status 1 dismissed       # alias: linkright jobs s 1 dismissed

# 6. Import your own jobs from CSV
linkright jobs import jobs.csv          # see schema below
linkright jobs import jobs.csv --dry-run  # validate only

Auth commands

linkright auth login                    # interactive (prompts method)
linkright auth login --method email     # email + password via Supabase
linkright auth login --method jwt       # paste JWT from browser DevTools
linkright auth status                   # show session info
linkright auth logout                   # clear session

CSV import schema

Column	Required?	Type	Example	Default if empty
title	YES	text	Senior Product Manager	(error — required)
company	YES	text	Razorpay	(error — required)
url	strong-recommend	text	https://razorpay.com/careers/abc	empty
location	optional	text	Bangalore	"Unknown"
jd_text	optional	text (multiline OK)	full JD body	(auto-fetched from URL if empty)
salary_min	optional	number (INR)	5000000	NULL
salary_max	optional	number	8000000	NULL
currency	optional	text	INR	INR
posted_date	optional	YYYY-MM-DD	2026-04-28	today
seniority	optional	text	senior / lead / mid	(auto-detected)
notes	optional	text	Referral via Anjali	empty
tags	optional	comma-separated	fintech,b2c	empty

Sample CSV:

title,company,url,location,notes
Senior Product Manager,Razorpay,https://razorpay.com/careers/abc,Bangalore,Referral via Anjali
Engineering Manager,PhonePe,https://phonepe.com/jobs/xyz,Mumbai,Applied via LinkedIn

After import, run linkright jobs find in 2-3 minutes to see fit scores (backend enriches asynchronously).

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Architecture

          ┌──────────────────────── linkright (Click CLI) ───────────────────────┐
          │                                                                       │
   user ──┤   resume   jobsearch   interview   content   init   mcp serve         │
          │      │          │           │         │                │              │
          │      └──────────┴───────────┴─────────┘                │              │
          │                       │                                │              │
          │              ┌────────┴─────────┐                      │              │
          │              │   llm.base       │                      │              │
          │              │ (mode router)    │                      ▼              │
          │              └───┬────────┬─────┘           ┌─────────────────┐       │
          │      direct ─────┘        └──── agent ─────▶│  FastMCP server │       │
          │        │                                    │  8 resume tools │       │
          │        ▼                                    └────────┬────────┘       │
          │  ┌──────────────┐                                    │                │
          │  │ LLM cascade  │  Groq → Gemini FL → Cerebras →     │                │
          │  │              │  SambaNova → Cloudflare → Z.ai →   │                │
          │  │              │  OpenRouter → Oracle gemma3:1b     │                │
          │  └──────┬───────┘                                    │                │
          │         │                                            │                │
          │         └──────────────┬─────────────────────────────┘                │
          │                        ▼                                              │
          │                 MongoDB (local)   ~/.linkright/                       │
          │              12 collections       config.yaml, cache, runs           │
          └───────────────────────────────────────────────────────────────────────┘

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Commands

Pillar 1 — Resume

Command	What it does
linkright resume tailor	16-step pipeline: parse JD → retrieve nuggets → write XYZ bullets → width-fit → score → emit HTML
linkright resume score	A–F scorecard (10 dims) on an existing resume against a JD
linkright resume batch	Run tailor across a folder of JDs
linkright resume iterate	Re-run with scorecard feedback loop

Pillar 2 — Job search

Command	What it does
linkright jobsearch evaluate	Score one JD vs your profile
linkright jobsearch recommend	Rank saved JDs by fit
linkright jobsearch apply	Log application + optional cover letter

Pillar 3 — Interview

Command	What it does
linkright interview schedule	Track an upcoming interview
linkright interview prep	Predicted questions + STAR retriever
linkright interview mock	Run a mock Q&A session
linkright interview debrief	Post-interview scorecard + retro

Pillar 4 — Content

Command	What it does
linkright content plan	Weekly content calendar
linkright content draft	Draft posts in your voice
linkright content schedule	Queue posts (stub — v0.4 APIs)
linkright content performance	Engagement report

Ops

Command	What it does
linkright init	Bootstrap ~/.linkright/ + Mongo collections
linkright mcp serve	Start per-session MCP server (agent mode)
linkright profile import	Parse resume → nuggets → embed → store

Legacy v0.0 commands (optimize, validate, assisted) are preserved.

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Configuration

File: ~/.linkright/config.yaml (created by linkright init)

Environment variables (Direct mode — 7-provider cascade, all free-tier first):

Var	Provider	Free tier	Cascade position
GROQ_API_KEY	Groq llama-3.3-70b	14,400 RPD	1 (primary)
GEMINI_API_KEY_1 / _2 / _3	Gemini Flash Lite (key rotation)	1000 RPD/key × 4	2
CEREBRAS_API_KEY	Cerebras qwen-235B + 8B	queue-based unlimited	3
SAMBANOVA_API_KEY	SambaNova Llama-3.3-70B	20 RPM	4
CLOUDFLARE_API_TOKEN + CLOUDFLARE_ACCOUNT_ID	Cloudflare Workers AI	10K Neurons/day	5
ZHIPU_API_KEY (or Z_AI_API_KEY)	Z.ai GLM-4.5-Flash	unlimited free tier	6
OPENROUTER_API_KEY	OpenRouter	$0 free models, $ paid	7 (last resort)
ORACLE_BACKEND_URL	Oracle Ollama (self-hosted gemma3:1b)	unlimited (your VPS)	local fallback

Forever-$0 path: signing up for Groq alone covers ~14,400 calls/day = ~2,000 resumes/day. Adding 2-3 more providers gives multi-tier defense against any single rate-limit. Drop a single key into ~/.linkright/.env and Config._autoload_env() picks it up automatically.

Agent mode (MCP server) needs none of these — the user's existing AI agent (Claude Code, Cursor, etc.) provides the LLM under their subscription quota.

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Data

MongoDB database: linkright — 12 collections:

nuggets            user_context       runs               jds
bullets_history    evaluations        applications       interviews
predicted_questions mock_sessions     content_items      content_calendar

File layout: ~/.linkright/

config.yaml        # user config
cache/             # LLM response cache
runs/              # per-run artifacts (resume HTML, JD parses, scorecards)
skills/            # installed skill packs (optional)

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Agent mode setup

LinkRight ships with .mcp.json pre-wired. To use:

Claude Code — open the repo; .mcp.json is auto-loaded. Say: "Tailor my resume for this JD". The tailor-resume skill fires, MCP server spawns, 8 tools are exposed.

Cursor — Settings → MCP → add this repo's .mcp.json.

Gemini CLI — point ~/.gemini/mcp.json at linkright mcp serve.

Skills live under .claude/skills/:

tailor-resume   score-resume   batch-apply   profile-refresh
evaluate-jd     interview-prep draft-posts   content-plan

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Known limits (v0.1)

• No web UI — CLI + MCP only
• English only
• No auto-submission to job boards
• Telemetry scorers are heuristic, not yet LLM-judged
• Single-user (no multi-profile)
• Pillars 2–4 are thin slices; only Pillar 1 is at iter-08 quality
• Vector search falls back to cosine-scan on local Mongo (Atlas-only feature)

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Roadmap

Version	Focus
v0.2	Full job-search scanner (recruiter channels, saved searches, auto-evaluate)
v0.3	Interview mock-session UX (voice, timed rounds, live scoring)
v0.4	Content scheduling APIs (LinkedIn, X) + engagement fetch
v1.0	Public PyPI release + stable agent skill contract
v2.0	Optional central sync + recruiter-side marketplace

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License

MIT — see LICENSE (TBD if missing).