---

name: avenir-web description: Use this skill to run and improve Avenir-Web autonomous web tasks end-to-end: choose mode (headless/headed/demo), write clear task instructions, execute single or batch runs, and propose high-impact next iterations.

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:

python example.py --task "<instruction>" --website "<url>" --mode headless

Atomic action:

python scripts/atomic_action.py --action CLICK --website "<url>" --coords "500,500"

Read page:

python scripts/read_page.py --website "<url>" --question "<question>"

Batch:

cd src
python run_agent.py -c config/batch_experiment.toml

Prefer these scripts over ad-hoc commands.

Quick usage example

Single task:

python example.py \
  --task "On openrouter.ai, list image-input-capable distillable models sorted by price ascending." \
  --website "https://openrouter.ai/" \
  --mode demo

Batch:

cd src
python run_agent.py -c config/batch_experiment.toml

Atomic action:

python scripts/atomic_action.py \
  --action TYPE \
  --website "https://example.com/" \
  --coords "500,420" \
  --value "hello"

Read page:

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 <website>, <objective>. Apply constraints: <constraints>. Finish when <observable completion state>.

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

[
  {
    "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.