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

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

[
  {
    "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.