Chatgpt Archive Topic Background Report

ChatGPT Archive Topic Background Report

Use this skill when a user wants end-to-end topic consolidation from ChatGPT archive data:

• Search relevant threads by topic in latest archive or all archives.
• Assemble a deduplicated JSON research collection.
• Run a background Responses API call with reasoning + verbosity + web search.
• Save markdown output and response payload to disk.

Preconditions

• Ensure OPENAI_API_KEY is set (environment or .env).
• Ensure outbound access to api.openai.com is available.
• Confirm chatgpt_viewer_sites/ exists with archive folders.

Core Workflow

1. Run the pipeline script with topic and archive scope:

python -u .agents/skills/chatgpt-archive-topic-background-report/scripts/run_topic_report.py \
  --topic "OpenClaw / Moltbot / Clawdbot" \
  --archive-scope latest

2. For all archives instead of latest:

python -u .agents/skills/chatgpt-archive-topic-background-report/scripts/run_topic_report.py \
  --topic "OpenClaw / Moltbot / Clawdbot" \
  --archive-scope all

3. Add custom prompt constraints (topic-specific or ad hoc):

python -u .agents/skills/chatgpt-archive-topic-background-report/scripts/run_topic_report.py \
  --topic "OpenClaw / Moltbot / Clawdbot" \
  --archive-scope all \
  --ad-hoc-requirements "Prioritize security incidents and timeline precision."

Quiet Monitoring Rule

• Keep polling output minimal by default.
• The script prints status changes and sparse heartbeats only.
• Use --verbose-poll only if detailed polling lines are explicitly needed.

Key Parameters

• --archive-scope latest|all: search latest archive or all archive folders.
• --keyword-regex: override keyword matching regex.
• --keyword: add extra keyword terms (repeatable).
• --model: default gpt-5.2.
• --reasoning-effort: none|low|medium|high|xhigh.
• --verbosity: low|medium|high.
• --search-context-size: low|medium|high for web search tool.
• --ad-hoc-requirements / --ad-hoc-requirements-file: append run-specific requirements.

Reporting Standard

• The default developer prompt template enforces an academic/journal-style report structure.
• Reports must state they are based on the provided research collection, augmented by targeted web search only when contradictions or ambiguities remain.
• Reports should avoid vague, repetitive references to "the dataset" and instead use precise source-language.

Outputs

Each run writes an output directory under scratchpad/topic_background_reports/<timestamp>_<topic>/ with:

• assembled_dataset.json (JSON artifact containing the provided research collection)
• developer_prompt_effective.txt
• user_task_effective.txt
• report.md
• response_payload.json
• run_manifest.json

Bundled Resources

• scripts/run_topic_report.py: complete pipeline (assemble -> background API -> poll -> save outputs).
• references/developer_prompt_template.txt: base developer prompt template with {{TOPIC}} placeholder.