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Sorokin"},"license":"UNLICENSED","homepage":"https://github.com/iLiveMyLife/linkedInJobsAutoApply#readme","keywords":["ilml","ilml-plugin","ilivemylife","knowledge-graph","personal-data","data-ownership","puppeteer","ai-assistant"],"repository":{"url":"git+https://github.com/iLiveMyLife/linkedInJobsAutoApply.git","type":"git"},"description":"Reference ilml plugin: keep a local mirror of your LinkedIn data and run AI-assisted workflows under your direction.","maintainers":[{"name":"silar","email":"ilya.sorokin@gmail.com"}],"readme":"# Your LinkedIn data — finally on your machine\n\nLinkedIn keeps your **connections, conversations, and the notes you write about people** locked inside their interface. You can't export message threads. You can't query an AI on your own contact history. You can't run batch operations from a script. You can't keep a personal database of who said what when.\n\nThis plugin pulls all of that onto your machine — into local JSON files that you fully own — and feeds derived signals (action plans, run reports, AI assistant context) into your [iLiveMyLife](https://ilivemylife.io) knowledge graph for AI-driven follow-up. Even if you uninstall the plugin or switch ilml accounts, **your accumulated data stays where you put it**.\n\nCommon LinkedIn workflows become scriptable: AI fills Easy Apply forms using context from your profile and notes, drafts message replies for your review before sending, and queues recruiter outreach under daily limits you configure.\n\n> **Note on intent and use.** This plugin demonstrates patterns for keeping a local mirror of your own LinkedIn data — syncing message history and connection metadata to JSON files on your machine — and for using AI to assist with form filling and message drafting on your behalf. It is published as a reference implementation for developers building plugins on top of the `ilml` SDK. Automated interaction with LinkedIn may conflict with their [User Agreement](https://www.linkedin.com/legal/user-agreement) — review it and run this at your own risk. Provided AS-IS; the author makes no warranty and accepts no liability for account actions, data loss, or other consequences resulting from use.\n\n---\n\n## What you get\n\n- **A full local mirror of your LinkedIn data.** Connections, conversation threads, message history, profile metadata, your private notes about people — stored in local JSON files you fully own; derived signals (action plans, AI context, run reports) flow into your iLiveMyLife graph for follow-up. Nothing stays trapped in linkedin.com.\n- **AI-assisted messaging — drafts go through you.** [Lifebot](https://ilivemylife.io) reads the full context of each thread (history, your notes, tags) and drafts replies in your voice. You review the batch in one pass and push approved drafts as a group. On push, the bot re-scans every thread first — if a new incoming message arrived since you reviewed, it pauses that draft and flags it for re-review instead of sending blind. Never autopilot.\n- **An auto-triaged inbox.** Every conversation gets classified (recruiter / hiring manager / founder / investor / spam / event), tagged, prioritized, and stamped with a suggested next action. `ilml linkedin today` prints a no-browser daily plan: who to reply to, what to do, in priority order — before you've even opened LinkedIn.\n- **Inbox sync that doesn't break your workflow.** By default `sync-all` pulls the conversation list and metadata without \"opening\" unread threads — LinkedIn keeps showing the unread badges until *you* read them in the UI. The sync report ends with a list of what's still unread so you can scan it from the terminal first. Use the bot as an analyst without losing your own attention markers. (Pass `--read-unread` if you want to vacuum unread threads into the local DB.)\n- **Terminal-first workflows.** Common tasks run as scripts you control — batched drafts you review before sending, queued applications under daily limits you set, scheduled runs that log everything. Repeatable and auditable.\n- **Easy Apply that actually thinks.** Auto-fills LinkedIn job applications, with Lifebot answering custom questions (\"Why are you interested in this role?\") in your voice using context from your graph — not boilerplate.\n- **Your data outlives the plugin.** The connection database, conversation history, and your notes live in a directory **you** choose (`DATA_DIR`). Plugin updates don't wipe them. Logging out of ilml doesn't wipe them. Uninstalling the plugin doesn't wipe them. Only **you** decide when they go.\n\n---\n\n## Prerequisites\n\n- **Node.js 18+** — [download](https://nodejs.org/) (the LTS version is fine).\n- **iLiveMyLife account** — free signup at [iLiveMyLife.io](https://ilivemylife.io).\n- **LinkedIn account** — your personal one.\n\nThat's it. There's nothing to clone or compile.\n\n## Install\n\nOne-time, on your machine:\n\n```bash\n# 1. Install the ilml CLI (skip if already installed)\nnpm install -g @ilivemylife/graph-sdk\n\n# 2. Log in to your iLiveMyLife account\nilml login\n\n# 3. Install this plugin\nilml plugin install linkedin\n```\n\nThe bare-name `linkedin` resolves to `npm:ilml-plugin-linkedin` via the ilml plugin convention. The CLI downloads the latest version from npm, extracts it under `~/.ilivemylife/plugins/linkedin/`, and runs `npm install` for runtime dependencies (mainly Puppeteer's bundled Chromium — first install takes a couple of minutes).\n\nVerify:\n\n```bash\nilml plugin list\nilml linkedin\n```\n\n## Configure\n\nThe first time you run `ilml linkedin <command>`, the CLI will prompt for required fields. You can also pre-configure:\n\n```bash\nilml plugin config linkedin set LINKEDIN_NAME \"Your Full Name\"\nilml plugin config linkedin set NODE_EASY_APPLY_MAIN <node-id>\nilml plugin config linkedin set NODE_EASY_APPLY_ERRORS <node-id>\n```\n\nInspect (secrets masked):\n```bash\nilml plugin config linkedin\n```\n\nChange later or wipe:\n```bash\nilml plugin config linkedin set LINKEDIN_NAME \"New Name\"\nilml plugin config linkedin unset OPTIONAL_KEY\nilml plugin config linkedin reset                  # re-prompt on next run\n```\n\n### Configuration fields\n\n| Section | Field | Required | What it is |\n|---|---|---|---|\n| LinkedIn account | `LINKEDIN_NAME` | yes | Your full name **exactly** as it appears on LinkedIn (used to identify your own messages in threads). |\n| | `LINKEDIN_LOGIN` | no | LinkedIn email — only used for auto-fill during one-time browser login. |\n| | `LINKEDIN_PASS` | no, secret | LinkedIn password — auto-fill only. Stored locally on disk; never sent anywhere. |\n| iLiveMyLife integration | `NODE_EASY_APPLY_MAIN` | yes | Node ID where job applications are tracked. See \"Setting up your graph\" below. |\n| | `NODE_EASY_APPLY_ERRORS` | yes | Node ID where Easy Apply errors are logged. |\n| | `NODE_RUN_REPORTS` | no | Node ID where end-of-session summaries are posted (enable notifications on it to get pings when long jobs finish). |\n| Data storage | `DATA_DIR` | no | Absolute path to a directory for your local databases (connections, conversations, profile cache). **Strongly recommended** — keeps your accumulated data outside the plugin install folder so it survives plugin updates, ilml logout, and reinstalls. |\n| Apply bot | `City` | no | Default city for filters. Default: `Toronto`. |\n| | `MAX_APPLY_FOR_RUN` | no | Stop after this many applications per run. Default: `50`. |\n| | `LINKEDIN_SEARCH_URL` | no | Default LinkedIn jobs search URL. Open LinkedIn → Jobs, set your filters, make sure Easy Apply is on, copy the resulting URL from the address bar. |\n| Scouting | `LINKEDIN_SCOUT_URLS` | no | Comma-separated job search URLs to scan without applying. |\n| Resume / CV | `CV_TEXT`, `RESUME_PDF_URL`, `RESUME_FILENAME` | no | CV snippet, public PDF URL, suggested filename. Used in messages to recruiters. |\n| Funnel | `FUNNEL_USE_CUSTOM_MESSAGE` | no | Send a custom note with connection requests. Default: `false`. |\n| | `FUNNEL_MAX_CONNECTIONS` | no | Cap connection requests per run. Default: `20`. |\n| | `FUNNEL_MESSAGE` | no | Note text. Use `{name}` placeholder, e.g. `Hi {name}, I just applied for a role at your company`. |\n| | `FUNNEL_EXCLUDE_LOCATIONS` | no | Comma-separated locations to skip. |\n| Connection limits | `CONNECT_MAX_DAILY` | no | Max connection requests per day across every path (`connect`, `funnel`, `apply`). Default: `20`. LinkedIn's real limit is dynamic and unknown — keep this under it, or raise it deliberately to surface LinkedIn's own limit (`connect` stops itself after 3 failures in a row and saves what LinkedIn showed to `connect-diagnostics/`). |\n| | `CONNECT_MAX_WEEKLY` | no | Rolling 7-day cap. Default: `100`. Free accounts are often limited around 100–200/week. Set e.g. `ilml plugin config linkedin set CONNECT_MAX_WEEKLY 250`. |\n| Discovery (experimental) | `DISCOVERY_ENABLED` | no | **Off by default.** When `true`, `connect run` also reads LinkedIn's \"People also viewed / More profiles for you\" side-panel on each profile it already opens and saves those people to a local discovery pool — no extra page loads, no effect on the connect itself. `connect fill --from-discovered` then tops up your queue from that pool, ranked by relevance to you. Adds ~2–3s per profile (a scroll to load the panel); English-locale LinkedIn only. Enable with `ilml plugin config linkedin set DISCOVERY_ENABLED true`. |\n\n## Setting up your graph\n\nThe Easy Apply bot uses iLiveMyLife's AI assistant (**Lifebot**) to answer custom application questions in your voice. Two graph nodes are required: a **main tracking node** with Lifebot enabled, and an **errors node** for failed applications.\n\n1. Open https://app.ilivemylife.io and create a node titled **\"LinkedIn Easy Apply\"** (or any name).\n2. Open the node's **settings** (right panel) and turn on **AI assistant**. This enables Lifebot for this node — without it, the bot can't answer questions and `ilml linkedin apply` will refuse to run.\n3. Copy the node ID from the URL: `https://app.ilivemylife.io/item/THIS-IS-THE-ID`.\n4. Set it: `ilml plugin config linkedin set NODE_EASY_APPLY_MAIN <id>`.\n5. In the node's **description**, write your answer rules — Lifebot reads this when filling forms. Example:\n\n   ```\n   Application question rules:\n   - My full name: Ivan Petrov\n   - City: Toronto, Ontario, Canada\n   - Phone: +1-416-xxx-xxxx\n   - Email: ivan@example.com\n   - Experience: Senior software engineer, 8 years\n   - Visa status: Permanent Resident, Canada\n   - Salary expectations: $120-150K CAD\n   - Notice period: 14 days\n   - Preferred work mode: hybrid or remote\n   ```\n\n6. (Optional but useful) Create child nodes inside \"LinkedIn Easy Apply\" with topical detail: `Resume`, `Tech stack`, `Interview availability`, etc. Lifebot reads the whole subtree and uses everything as context.\n\n7. Create a sibling node titled **\"Easy Apply Errors\"** (no AI needed). Copy its ID and set it: `ilml plugin config linkedin set NODE_EASY_APPLY_ERRORS <id>`.\n\n### Save AI tokens — tune the node settings\n\nLifebot uses AI tokens for every form question it answers. Two settings on the Easy Apply node let you tune cost vs. quality without touching the plugin:\n\n```bash\n# Use a cheaper, faster AI model (good enough for filling forms):\nilml editSettings <NODE_EASY_APPLY_MAIN> --intelligence disabled\n\n# Skip whole-graph context lookup — just use this node's description:\nilml editSettings <NODE_EASY_APPLY_MAIN> --rootAccess disabled\n```\n\n`intelligence: disabled` switches Lifebot to a cheaper model (think GPT-3.5 / Claude Haiku tier instead of GPT-4 / Claude Opus). For application form filling, the cheap model is usually fine — questions are mechanical.\n\n`rootAccess: disabled` stops Lifebot from searching your entire knowledge graph for context every time it answers. The Easy Apply node's description and child nodes already have everything Lifebot needs — extra context costs tokens for no benefit.\n\nTo switch back: pass `--intelligence enabled` or `--rootAccess enabled`. To reset to your account default: pass `null`.\n\n## First-time LinkedIn login\n\nLinkedIn requires an authenticated browser session, so a one-time login is needed:\n\n```bash\nilml linkedin login\n```\n\nA real browser window opens. Sign in to LinkedIn (manually solve any captcha or 2FA). When you see your feed, **come back to this terminal and press Enter** to save your session — don't close the browser window yourself, cookies are captured on Enter and the window then closes automatically. (If the profile is already signed in, the command detects it and just asks you to press Enter to re-save.)\n\nSessions live in your active ilml scope (`<scope>/.ilivemylife/plugins-state/linkedin/cookies.json`), so:\n\n- Different ilml scopes (e.g. `ilml login` globally vs `ilml login --local` in a project folder) keep **separate** LinkedIn sessions — useful if you have multiple LinkedIn accounts.\n- Plugin updates do **not** wipe your session — you don't need to re-login after `ilml plugin update linkedin`.\n- `ilml logout` does wipe the session in that scope (alongside other user-tied data) — clean handoff between users on a shared machine.\n\n### The browser profile (one trusted device)\n\nTo keep LinkedIn treating the automation as **one consistent device** — instead of a fresh \"new device\" every run (which triggers \"new device\" emails, early session drops, and higher ban-risk) — the plugin keeps a **persistent Chrome profile** per account, next to your session:\n\n```\n<scope>/.ilivemylife/plugins-state/linkedin/chrome-profile/<account>/\n```\n\nYou normally never touch it. Two things worth knowing:\n\n- **Resetting it:** if the profile ever gets stuck or corrupted (e.g. Chrome crashed mid-run), delete that `chrome-profile/` folder. The next command rebuilds a clean profile and seeds it from your saved session automatically — no re-login needed. Only if the saved session has also expired do you run `ilml linkedin login` to sign in again.\n- **Pointing it elsewhere:** set `LINKEDIN_CHROME_PROFILE_DIR` to an absolute path to store the profile somewhere specific (e.g. a faster disk). Optional — leave it unset to use the default location above.\n\n## Daily commands\n\n| Command | What it does |\n|---|---|\n| `ilml linkedin apply` | Auto-apply to LinkedIn Easy Apply jobs from `LINKEDIN_SEARCH_URL`. Stops after `MAX_APPLY_FOR_RUN`. |\n| `ilml linkedin sync-all` | Pull the LinkedIn inbox into the local DB and the graph. By default it preserves LinkedIn's unread badges (unread threads aren't opened) and lists what's still unread at the end of the report. Pass `--read-unread` to vacuum unread threads into the DB too (loses the badges). `--full` for a full re-scan, `--report` for stats only with no browser. |\n| `ilml linkedin today` | No-browser daily plan: who to reply to, what to do, in priority order. Reads already-synced data — doesn't open LinkedIn. |\n| `ilml linkedin enrich` | Re-classify every conversation (recruiter / hiring manager / founder / investor / spam / event) and rebuild priorities, summaries, and suggested actions. Offline, no browser. |\n| `ilml linkedin report` | Print the summary of the latest session. |\n| `ilml linkedin version` | Print the installed plugin version — confirm an update actually landed. |\n| `ilml linkedin scout` | Scan jobs from `LINKEDIN_SCOUT_URLS` and score them — without applying. Builds a queue for `apply-queue`. |\n| `ilml linkedin apply-queue` | Apply to the top-scored jobs from the latest `scout` run. |\n| `ilml linkedin messages` | Batch messaging — draft, review, then push. Nothing sends without your approval. Common forms: `messages --review-drafts` (review pending drafts) and `messages --push-drafts` (push approved). |\n| `ilml linkedin funnel` | Run the recruiter / founder / investor connection-request queue. |\n| `ilml linkedin connect` | Build a reviewable, prioritised queue of people to connect with, then send under your daily/weekly quota. `connect add <file.json>` drops a curated list; `connect fill --category=founder,investor,hiring_manager` (or `--valuable`) pulls from people already categorised in your contacts; `connect fill --from-discovered` tops up from the discovery pool (see `DISCOVERY_ENABLED`), ranked by relevance to you; `connect list` / `approve` / `note` / `priority` / `skip` manage the queue; `connect run` sends only the approved ones. Sends go **without a note** by default (LinkedIn caps custom notes to a few per month) — save notes on the deliberate few with `connect note` and send them with `connect run --with-notes`. Every send goes through the same quota gate; `run` stops itself after 3 failures in a row (often LinkedIn's own limit). |\n| `ilml linkedin quota` | No-browser report: how much of today's / this week's connection quota is left and how it was spent (by source), the acceptance rate per source (which outreach actually converts), and high-value contacts you haven't connected to yet. `--json` for machine output. |\n| `ilml linkedin visit` | Visit recruiter / target profiles so they see you in \"who viewed your profile\". |\n| `ilml linkedin viewers` | Pull the \"who viewed your profile\" list. |\n| `ilml linkedin warm-scan` | Find 1st-degree connections at target companies (offline, no browser). Reads your already-synced people / conversations and groups them by company so you know who you can message directly versus where you need cold outreach. Give it the companies with `--companies=\"Anthropic,OpenAI\"`, or a named group you saved in your own `<DATA_DIR>/target-companies.json` via `--group=<name>`. |\n| `ilml linkedin daily` | Full pipeline in one command: `sync-all` → `apply` → `scout` → `funnel` → `visit` → `viewers` → run report. |\n| `ilml linkedin enrich-profile <url> [--with-experience]` | Visit ONE profile by URL, save what we learn to `people.json`. Self-profile (recognised via `MY_LINKEDIN_URL` / `LINKEDIN_NAME`) prints to stdout instead of saving. `--with-experience` adds a structured experience array (2 page loads, normal human pattern). Single-URL only — no batch mode. `--help` for full details. |\n| `ilml linkedin enrich-profiles [--with-experience]` | **⚠ HIGH BAN RISK** — batch-visit contacts with missing About text, fill basic fields. Cap 10/session by default (`--max=N` to change). `--with-experience` doubles per-profile page loads. `--connections` for lower risk (1st-degree only). `--match-title=\"recruiter\\|founder\"` to filter by role. `--list` for stats-only. `--help` for full details. |\n| `ilml linkedin login` | Re-save LinkedIn cookies after a session expires. |\n\nRun `ilml linkedin` (no subcommand) any time to see the list. Any command above supports `--help` for full usage and flags (the new `enrich-profile` / `enrich-profiles` definitely do; older commands print usage when called with bad arguments).\n\n### For AI agents using this plugin\n\nIf an AI agent is figuring out how to drive this plugin, the canonical machine-readable command surface lives in [`ilml-plugin.json`](./ilml-plugin.json). The `commands` object includes per-command `description`, `usage`, `flags`, `banRisk`, and `useWhen` fields for the higher-stakes commands (`enrich-profile`, `enrich-profiles`) — read that before invoking. The `config.sections[].fields[]` array tells you which env vars are required vs optional and what each does. Per-command `--help` is the fast way to see the flag list interactively.\n\n## Common routines\n\nA few patterns that string the daily commands together. Outgoing messages always require your explicit approval (via `messages --push-drafts`); commands like `apply` and `funnel` do submit Easy Apply forms and connection requests on their own, scoped by `MAX_APPLY_FOR_RUN` and `FUNNEL_MAX_CONNECTIONS`.\n\n**Morning triage** — see what needs attention before opening LinkedIn:\n```bash\nilml linkedin sync-all   # pull new messages; unread threads stay bold and are listed at the end\nilml linkedin today      # print prioritized day plan\n```\n\n**Active job search** — apply, scout, network on autopilot; you stay in the loop on outgoing messages:\n```bash\nilml linkedin daily                       # apply + scout + funnel + visit + viewers + report\nilml linkedin messages --review-drafts    # later: review the AI's drafted replies\nilml linkedin messages --push-drafts      # push the ones you approved\n```\n\n**Passive pipeline** — keep the inbox synced and triaged without touching outgoing channels:\n```bash\nilml linkedin sync-all\nilml linkedin enrich\nilml linkedin today\n```\n\n**Targeted networking** — figure out which target companies you can reach via existing connections before sending any cold outreach:\n```bash\nilml linkedin warm-scan --companies=\"Anthropic,OpenAI,Stripe\"   # scan these companies\nilml linkedin warm-scan --group=<name>        # a named group from your own <DATA_DIR>/target-companies.json\n```\nYou pass the companies you care about — nothing is hardcoded. Optionally save named groups in `<DATA_DIR>/target-companies.json` (your data) and use them with `--group`.\n\nCompose your own from the commands above — each one is a standalone script.\n\n## Updating\n\nUpdates ship via npm. The CLI checks the registry and only downloads when a newer version is actually available:\n\n```bash\nilml plugin update linkedin    # check this plugin\nilml plugin update             # check all installed plugins\nilml update                    # update the SDK and all plugins together\n```\n\nYou'll see one of:\n- `linkedin: already on latest version (1.3.0)` — nothing downloaded\n- `linkedin: 1.3.0 → 1.3.1` then re-install — newer version pulled\n\n**Your config and your data both survive updates.** Only the plugin code dir gets refreshed; everything user-tied lives elsewhere.\n\n### Schema migrations\n\nSome updates change the on-disk format of your local data. The plugin runs needed migrations automatically the first time you invoke any command after an update — you don't have to do anything. Several safety nets are in place every time:\n\n1. **Pre-migration integrity check.** Before anything else, every `.json` file in `DATA_DIR/collected-profiles/` and `DATA_DIR/market-research/` is parsed as JSON. If any file is corrupted (truncated, hand-edited and broken, hit by disk failure, etc.) the migration aborts before touching anything — you get a list of which files are broken so you can repair them first. We don't back up corrupted state and we don't try to migrate it.\n2. **Pre-migration backup.** Once integrity is confirmed, every regular file in both data dirs is atomically copied to `<filename>.pre-vNNN.backup` (where `NNN` is the migration version about to run). Originals are left untouched.\n3. **Atomic write + crash-safe.** Migrations and backup copies use a `.tmp` file + rename, so a crash mid-write never leaves a half-written file. If a migration throws, the runner exits with an error pointing at the backups; your originals are intact.\n\nManual operations:\n\n```bash\nilml linkedin migrate-status              # what schema version you're on, what's pending, what backups exist, what leftovers can be cleaned\nilml linkedin data-cleanup                # remove orphan .tmp files + pre-rollback snapshots (does NOT touch pre-migration backups)\nilml linkedin rollback-data               # preview what a DATA rollback would do (read-only)\nilml linkedin rollback-data --confirm     # actually roll back DATA — does NOT touch plugin code\n```\n\n`DATA_DIR` is kept tidy automatically — orphan `.tmp` files (leftovers from a crashed atomic write) are swept on every plugin start. Pre-migration backups (`.pre-vNNN.backup`) are kept indefinitely because `rollback-data` needs them; you can remove them manually if you're sure you'll never roll back. Pre-rollback snapshots (`.pre-rollback-vNNN.backup`) are kept as a \"I changed my mind\" safety net after a rollback and can be removed with `data-cleanup` once you're confident the rollback is good.\n\n### Full rollback procedure (two steps)\n\n`rollback-data` deliberately separates DATA rollback from CODE rollback. To fully revert to a previous plugin version you must do BOTH, in this order:\n\n```bash\n# Step 1 — roll back data (only run on the new plugin version; older versions don't have this command)\nilml linkedin rollback-data --confirm\n\n# Step 2 — IMMEDIATELY downgrade the plugin code, BEFORE running any other ilml linkedin command\nilml plugin install linkedin@<previous-version>\n```\n\nBetween step 1 and step 2: **do not run any `ilml linkedin` command.** The current plugin will see the rolled-back data, detect a \"missing\" migration, and immediately re-apply it — silently undoing your rollback.\n\n**`rollback-data` is destructive** — it overwrites your current files with the backup taken before the last migration. Any data added after that migration (new conversations synced, drafts written, notes added) will be lost. Rollback uses an all-or-nothing strategy: every backup file is JSON-validated upfront; if any one is corrupted, rollback aborts before touching any original (avoids \"half restored, half new\" inconsistency). Once validation passes, your current state is also snapshotted into `<filename>.pre-rollback-vNNN.backup` files as a safety net before the atomic restore.\n\n**Just want to inspect old data?** Don't roll back. Copy your `DATA_DIR` somewhere else and run a separate `DATA_DIR=/copy/path` plugin instance against that copy — your live setup stays untouched.\n\n## Where things live\n\n| Path | What's there | Wiped on plugin update? | Wiped on `ilml logout`? |\n|---|---|---|---|\n| `~/.ilivemylife/plugins/linkedin/` | Plugin code | yes (re-extracted) | no |\n| `<scope>/.ilivemylife/plugins-state/linkedin.json` | Your configured values (LINKEDIN_NAME, node IDs, etc.) | no | yes (with that scope) |\n| `<scope>/.ilivemylife/plugins-state/linkedin/cookies.json` | LinkedIn session cookies | no | yes (with that scope) |\n| Directory you set as `DATA_DIR` | Local DBs, synced profile metadata, message history, your notes | **no** | **no** |\n\nIf you didn't set `DATA_DIR`, runtime data files default to inside the plugin's install dir — meaning a plugin reinstall **wipes them**. Setting `DATA_DIR` to a sibling folder like `~/linkedin-plugin-data/` is **strongly recommended**:\n\n```bash\nilml plugin config linkedin set DATA_DIR /absolute/path/to/your/linkedin-data\n```\n\nOnce set, you can sync `DATA_DIR` to a backup drive, a cloud folder, or another machine — the data is plain SQLite-style JSON, fully portable.\n\n## Removing the plugin\n\n```bash\nilml plugin remove linkedin\n```\n\nThis wipes the install dir. Your config in the active scope:\n```bash\nilml plugin config linkedin reset\n```\n\nThe data in `DATA_DIR` is yours — delete the folder manually if you want it gone, or keep it.\n\n---\n\n## Feedback & support\n\nBug reports, feature requests, and general questions land in the plugin's **Support** node inside iLiveMyLife. Send a message there and we'll get back to you:\n\n- In any iLiveMyLife app (web / mobile / desktop): open https://app.ilivemylife.io/item/0000019dde81b04f-2e534bb8ebad0000 and use the messenger panel\n- From the terminal: `ilml send 0000019dde81b04f-2e534bb8ebad0000 \"your message\"`\n\n## Links\n\n- This plugin on npm: https://www.npmjs.com/package/ilml-plugin-linkedin\n- `ilml` CLI on npm: https://www.npmjs.com/package/@ilivemylife/graph-sdk\n- iLiveMyLife: https://ilivemylife.io\n\n---\n\n## Maintained by\n\n[**DigitalTwins.team**](https://www.digitaltwins.team/) — independent\nconsultancy that builds and maintains `ilml` plugins, automation tools,\nand bespoke integrations. This plugin is one of several reference\nimplementations published as part of that work.\n\nFor commercial integrations, custom plugin development, or contract\nengineering inquiries → **info@DigitalTwins.team**.\n\nFor bug reports or plugin questions, use the in-graph Support node\nlinked above.\n","readmeFilename":"README.md"}