# X Growth Operator

> Plan and execute X growth operations for OpenClaw. Use when the user wants to monitor KOL posts, detect emerging discussions, turn briefs or uploaded docs into a growth mission, draft replies/posts, rank opportunities, and execute approved actions with a local audit trail.

- Skill: `dvcrn/x-growth-operator` (Agent Skill, multi-file: 28 files)
- Install (CLI): `npx skillmds@latest add dvcrn/x-growth-operator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/x-growth-operator/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/x-growth-operator

---


# 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:

```bash
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:

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

Import live opportunities with Desearch:

```bash
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:

```bash
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:

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

3. Draft a recommended action:

```bash
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:

```bash
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:

```bash
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:

```bash
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:

```bash
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:

```bash
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