# Princeton Ai2 Lab Avenir Web Avenir Web > Avenir-Web - Skill: `tomevault-io/princeton-ai2-lab-avenir-web-avenir-web` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/princeton-ai2-lab-avenir-web-avenir-web` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/princeton-ai2-lab-avenir-web-avenir-web/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/princeton-ai2-lab-avenir-web-avenir-web --- # Avenir-Web ## What this skill does This skill operates Avenir-Web for reliable web-task execution and iteration. Responsibilities: - run single tasks and batch tasks - choose mode (`headless` / `headed` / `demo`) - improve instruction quality before execution - analyze run outputs and recommend the next best change - execute one atomic action without strategy/checklist overhead - read the current page by screenshoting it and asking the main model a question Use this skill for requests like: - run a task on a website - run a task list and summarize outcomes - improve success rate with better instructions/config ## Canonical entrypoints Single task: ```bash python example.py --task "" --website "" --mode headless ``` Atomic action: ```bash python scripts/atomic_action.py --action CLICK --website "" --coords "500,500" ``` Read page: ```bash python scripts/read_page.py --website "" --question "" ``` Batch: ```bash cd src python run_agent.py -c config/batch_experiment.toml ``` Prefer these scripts over ad-hoc commands. ## Quick usage example Single task: ```bash python example.py \ --task "On openrouter.ai, list image-input-capable distillable models sorted by price ascending." \ --website "https://openrouter.ai/" \ --mode demo ``` Batch: ```bash cd src python run_agent.py -c config/batch_experiment.toml ``` Atomic action: ```bash python scripts/atomic_action.py \ --action TYPE \ --website "https://example.com/" \ --coords "500,420" \ --value "hello" ``` Read page: ```bash python scripts/read_page.py \ --website "https://openrouter.ai/" \ --question "What models or prices are visible on this page?" ``` ## Run modes | mode | behavior | best for | |---|---|---| | `headless` | no visible browser window | fast, reproducible runs and large batch jobs | | `headed` | visible browser window | manual observation without demo overlay | | `demo` | visible window + overlay/dashboard controls | live debugging and demonstrations | Notes: - if mode is missing, use `headless` - `demo` improves observability, not model intelligence Mode selection: 1. benchmark/batch -> `headless` 2. visual debugging -> `headed` 3. demo/control flow visibility -> `demo` ## Instruction design `confirmed_task` should include: 1. objective 2. constraints 3. completion condition Template: - `On , . Apply constraints: . Finish when .` Keep it single-goal, specific, and verifiable. ## Single-task workflow Input: - `task` - `website` - optional `mode`, `task-id`, `output-dir` Steps: 1. check environment and API key 2. validate instruction quality 3. run `example.py` 4. inspect outputs 5. report status + cause + next action Recommended report fields: - `task_id` - status: `success` / `partial` / `failed` - evidence summary - one-line cause - one recommended next step ## Atomic action workflow Use `scripts/atomic_action.py` when you need exactly one browser operation and do not want strategist/checklist generation. Typical uses: - one click - one type - one goto - one scroll Properties: - disables strategy generation - disables checklist generation - executes exactly one action - returns structured JSON with result, URL, screenshot path, and output directory ## Read-page workflow Use `scripts/read_page.py` when you want to inspect the current page by screenshot and ask the main model a direct question. Properties: - opens the page - captures a screenshot - sends the screenshot plus page metadata to the main model - returns structured JSON with the answer and screenshot path ## Batch workflow ### Task file schema ```json [ { "task_id": "example_task_001", "confirmed_task": "Find image-input-capable distillable models sorted by price ascending.", "website": "https://openrouter.ai/" } ] ``` Required per task: - `task_id` - `confirmed_task` - `website` ### Config checklist (`src/config/batch_experiment.toml`) - `[basic].save_file_dir` - `[experiment].task_file_path` - `[experiment].max_op` - `[playwright].mode` - `[model].name` - API key source ### Batch execution 1. validate JSON schema and config paths 2. choose mode and `max_op` 3. run batch command 4. summarize per-task outcomes 5. provide one global improvement recommendation Recommended batch report fields: - total/completed/failed counts - per-task status list - recurring issue patterns - one highest-impact next change ## API requirements Required credential: - `OPENROUTER_API_KEY` (preferred) Resolution order: 1. environment variable `OPENROUTER_API_KEY` 2. `[api_keys].openrouter_api_key` in TOML (fallback) Rules: - never hardcode real keys in source files - never print full keys in logs/outputs/reports - fail fast if key is missing with an actionable message ## Script usage rules - script-first: use repository entrypoints before custom commands - non-interactive CLI only - explicit flags and paths - deterministic behavior preferred - clear, actionable error messages If adding helper scripts: 1. place under `scripts/` 2. use CLI flags (no prompts) 3. return stable, parseable summaries 4. document usage in this file ## Environment checklist Before running: 1. Python environment available 2. dependencies installed (`pip install -e src`) 3. Playwright Chromium installed (`python -m playwright install chromium`) 4. API key configured 5. config/task paths valid ## Output contract Each run summary should include: 1. execution metadata: run type, mode, task IDs 2. outcome: status and evidence summary 3. diagnosis: root-cause hypothesis 4. next action: one highest-impact recommendation ## Boundaries - do not claim completion without evidence - do not skip issue summary - avoid large refactors before instruction/config fixes - avoid interactive prompts in core workflow ## One-line identity Avenir-Web execution and reliability skill: mode selection + instruction design + run analysis + iteration planning. --- > Source: [Princeton-AI2-Lab/Avenir-Web](https://github.com/Princeton-AI2-Lab/Avenir-Web) — distributed by [TomeVault](https://tomevault.io).