prakhar1114/ai_mime_marketplace/reply-on-x

Reply on X

Preconditions

• The user must already be signed in to X in the Chrome session controlled by browser-harness.
• The target X post/comment must be replyable.
• Running this skill posts a real public reply. Reruns are not idempotent, and X may block repeated duplicate reply text.

Inputs

• post_url: X status URL containing /status/<id>.
• reply_text: Text to post as the reply.

Run

Use:

./run.sh /path/to/inputs.json

If no path is provided, run.sh uses inputs/inputs.example.json.

run.sh uses the first available Python interpreter in this order:

1. Skill .venv/bin/python
2. Workflow .venv/bin/python
3. Required $AI_MIME_PYTHON_PATH

This skill has no third-party Python package requirements. If requirements.txt is added later, install or repair dependencies during skill build or manual repair with:

"$AI_MIME_UV_PATH" venv .venv --python "$AI_MIME_PYTHON_PATH"
"$AI_MIME_UV_PATH" pip install -r requirements.txt --python .venv/bin/python

Runtime does not create or repair .venv.

Outputs

• reply_url: URL of the newly posted X reply.

The script also prints the final output JSON to stdout.

Progress log format

Structured progress events are emitted to stderr:

{"event":"step_start","id":"load_inputs","title":"Load inputs"}
{"event":"step_done","id":"post_reply","outputs":{"reply_url":"https://x.com/.../status/..."},"summary":"Reply posted"}
{"event":"workflow_done","outputs":{"reply_url":"https://x.com/.../status/..."}}

Failures emit:

{"event":"step_failed","id":"post_reply","error":"...","recoverable":true}

Fallback

If run.sh fails, read references/fallback_plan.md. The main manual fallback is to open the supplied X status URL in logged-in Chrome, scroll the inline reply composer into view, enter reply_text, submit with Cmd+Return or the Reply button, then copy the URL from the newly posted reply.

ask_llm decision points

None. The skill does not call ask_llm.

References

• references/fallback_plan.md: manual/UI-agent recovery flow, selectors, and edge cases learned during validation.