X Growth Operator

Use this skill when the user wants an OpenClaw workflow for X operations rather than a generic writing assistant.

Purpose

Turn a brief into an X growth mission, find relevant opportunities, draft actions, and execute approved posts with an audit trail.

Trigger Conditions

Use this skill when the user asks to:

• operate or grow an X account
• monitor KOLs, keywords, or emerging discussions
• react to breaking events on X
• draft replies, quote posts, or original posts for X
• create or update a mission from a brand brief or uploaded document
• test an autonomous-but-reviewed X growth workflow in OpenClaw

Workflow

Run these steps in order unless the user explicitly asks for one step only.

1. Build or refresh the mission:

python3 scripts/ingest_goal.py \
  --doc examples/brand_brief.md \
  --mission data/mission.json

You may also pass --prompt instead of --doc.

2. Gather opportunities:

python3 scripts/watch_x.py \
  --mission data/mission.json \
  --input examples/opportunities.json \
  --output data/opportunities_scored.json

Import live opportunities with Desearch:

python3 scripts/import_desearch.py \
  x "openclaw OR local agent" \
  --count 10 \
  --output data/opportunities_from_desearch.json

Or run live search all the way to an action plan:

python3 scripts/live_search_and_plan.py \
  --mission data/mission.json \
  --query "openclaw OR local agent OR coding agent" \
  --count 10

If the operator is manually surfing X and taking notes, convert those notes first:

python3 scripts/import_surf_notes.py \
  --notes examples/surf_notes.md \
  --output data/opportunities_from_notes.json

3. Draft a recommended action:

python3 scripts/propose_action.py \
  --mission data/mission.json \
  --opportunities data/opportunities_scored.json \
  --opportunity-id opp-openclaw-breakout \
  --output data/action.json

4. Execute only after explicit user approval:

python3 scripts/execute_action.py \
  --mission data/mission.json \
  --action data/action.json \
  --log data/execution_log.jsonl

For real X execution, first install Node dependencies and configure X OAuth credentials, then run:

python3 scripts/check_env.py --mode execution
python3 scripts/execute_action.py \
  --mission data/mission.json \
  --action data/action.json \
  --log data/execution_log.jsonl \
  --approved \
  --mode x-api

5. Build a ranked action plan from the current opportunity set:

python3 scripts/plan_actions.py \
  --mission data/mission.json \
  --opportunities data/opportunities_scored.json \
  --output data/action_plan.json

6. Learn from feedback and refresh memory:

python3 scripts/review_feedback.py \
  --mission data/mission.json \
  --feedback examples/feedback.json \
  --memory data/memory.json \
  --output data/feedback_report.json

7. Run the whole cycle in one command when testing:

python3 scripts/run_cycle.py \
  --doc examples/brand_brief.md \
  --opportunities examples/opportunities.json \
  --feedback examples/feedback.json

Rules

• Default to review mode. Do not execute posts or replies until the user clearly approves the final action.
• If the user provides a document, parse it into mission fields first instead of drafting content immediately.
• If no live X source is available, operate on imported JSON opportunities and keep the workflow moving.
• Prefer replies and quote posts over net-new posts when the opportunity is time-sensitive.
• Reject actions that conflict with mission constraints, banned topics, or risk thresholds.
• When confidence is low, recommend observe instead of forcing an action.
• Persist what worked. Use data/memory.json to carry forward successful angles, source accounts, and action types.
• Treat auto execution as future work. This skill is review-first.
• Real X execution requires X_API_KEY, X_API_SECRET, X_ACCESS_TOKEN, and X_ACCESS_TOKEN_SECRET.

Resources

• examples/brand_brief.md: sample source document for mission ingestion
• examples/opportunities.json: sample X opportunities for local testing
• examples/feedback.json: sample outcome data for memory updates
• examples/surf_notes.md: sample manual browser-surf notes
• examples/desearch_query.txt: sample live search query
• references/mission-schema.md: field meanings and scoring rules
• scripts/.env.example: example execution environment file

Outputs

• data/mission.json: structured mission for later turns
• data/opportunities_scored.json: ranked opportunities with reasons
• data/action_plan.json: ranked next actions for the current cycle
• data/action.json: one reviewed action proposal
• data/execution_log.jsonl: append-only execution history
• data/memory.json: persistent learned patterns and historical outcomes