Moltbook Digest#

Use this skill when the user wants more than a scrape. The goal is to turn Moltbook discussions into a usable evidence pack and then into a clear report.

When To Use It#

• query-driven research on a specific Moltbook topic
• feed digest for hot, new, top, or rising
• repeated monitoring for one submolt or topic
• agent-written report from a collected evidence pack

Core Rule#

Prefer Moltbook's public API over browser scraping:

1. collect candidate posts
2. expand the strongest posts and comments
3. interpret the evidence

Do not claim exhaustive coverage unless the actual sample supports it.

Setup#

Install dependencies:

bash

uv sync --project "{baseDir}"

Before any interpreted run, create a user-specific config:

bash

cp "{baseDir}/config.example.yaml" "{baseDir}/config.yaml"

Then customize config.yaml:

• replace analysis.default_language: "__USER_PREFERRED_LANGUAGE__"
• keep or change active_provider
• adjust analysis.question_template, analysis.contract_template, and analysis.report_structure if needed
• fill provider keys only when using an external provider

Do not run interpretation directly from config.example.yaml. Do not ask the agent to write or reveal API keys.

Short Commands#

Collection only:

bash

uv run --project "{baseDir}" python "{baseDir}/scripts/moltbook_digest.py" \
  --query "agent memory architecture" \
  --query "agent memory failures and tradeoffs" \
  --analysis-mode none

Feed digest:

bash

uv run --project "{baseDir}" python "{baseDir}/scripts/moltbook_digest.py" \
  --collection-mode feed \
  --feed-sort hot \
  --max-posts 5 \
  --comment-limit 6 \
  --analysis-mode none

Continuous tracking:

bash

uv run --project "{baseDir}" python "{baseDir}/scripts/moltbook_digest.py" \
  --collection-mode feed \
  --feed-sort rising \
  --submolt agents \
  --history-dir output/moltbook-digest/history \
  --analysis-mode none

Interpretation Paths#

Agent#

Use this when the current agent should write the final report.

bash

uv run --project "{baseDir}" python "{baseDir}/scripts/moltbook_digest.py" \
  --query "agent memory governance" \
  --analysis-mode auto \
  --llm-config "{baseDir}/config.yaml"

What the script writes:

• digest.md
• evidence.json
• analysis_input.md
• agent_handoff.md

What the calling agent must do next:

1. read agent_handoff.md first
2. read analysis_input.md
3. write the final report to analysis_report.md

Do not draft the report from digest.md alone.

LiteLLM#

Use this when the script should call an external provider.

bash

uv run --project "{baseDir}" python "{baseDir}/scripts/moltbook_digest.py" \
  --query "long-running agent memory patterns" \
  --analysis-mode auto \
  --llm-config "{baseDir}/config.yaml"

What the script writes:

• digest.md
• evidence.json
• analysis_input.md
• analysis_report.md

Output Contract#

This test build uses fixed filenames:

• digest.md
• evidence.json
• analysis_input.md
• agent_handoff.md
• analysis_report.md

In agent mode, analysis_report.md is not auto-generated by the script. It is the expected output path for the calling agent.

Guidance For The Calling Agent#

If the user is vague:

• ask what question the report should answer
• ask whether the goal is breadth, depth, or recency
• ask whether a specific submolt matters

If the user does not answer:

• make one reasonable assumption
• state it clearly in the report

Notes#

• references/api.md contains endpoint notes and query guidance
• search and expansion are fault-tolerant
• non-fatal issues are recorded in evidence.json