x-intelligence-reporting

name

x-intelligence-reporting

description

Summarize X/Twitter intelligence digests for ML, AI, frontier/foundation model practice, agents, evals, tool use, harness engineering, loop engineering, and loopcraft. Use when asked to run or interpret the loopcraft X intelligence workflow, prepare a daily Codex automation report from X posts, highlight frontier-lab posts, discover follow candidates, or extract direct links from X posts and linked external articles/blogs/papers.

X Intelligence Reporting

Workflow

1. Work in the loopcraft source tree from the runtime context (the repo with Makefile, config/, and src/) unless the user gives another workspace.
2. Fetch a fresh digest by running the direct CLI: PYTHONPATH=src python -m loopcraft.research_intel.x.cli run --config config/x_intel.yaml. You are executing inside a headless loop run, so do not re-enter the control plane for this loop (no loopctl run / make run of x-intel) — that recurses. (The control plane also enforces this programmatically: it sets LOOPCRAFT_ACTIVE_LOOP for the run and refuses re-entry into the same loop while it is set.) If network access or X API credentials fail, report that first.
3. Read state/research/x/latest.md and latest.json from the loopcraft memory ledger (or the emitted direct-CLI paths). Prefer JSON for exact fields and Markdown for human-readable ordering.
4. When useful, run PYTHONPATH=src python -m loopcraft.research_intel.x.cli discover-follows --config config/x_intel.yaml and read the emitted follow-candidate Markdown/JSON paths.
5. When summarizing, include both the X post permalink and any external links from entities.urls[*].expanded_url or Markdown External links: lines.
6. Group findings by theme rather than only by score. Use these default themes:
   • Frontier-lab signal
   • Loop engineering and long-running agents
   • Harnesses, orchestration, verifiers, and memory
   • Evals, observability, traces, and post-training
   • Tool use, local systems, and production practice
7. Highlight frontier-lab posts separately. Treat frontier-lab attribution as explicit handle/config metadata, not loose profile-text inference.
8. Include follow recommendations when available. For each candidate, include why they surfaced and at least one evidence post link.
9. Call out practical loopcraft implications: what to read, what to implement, what to watch, and what should influence automation or agent-harness design.

Reporting Standards

• Lead with errors, rate limits, auth failures, or empty fetches if present.
• Mention generated date and fetched/ranked counts when available.
• Link every cited X post using https://x.com/{username}/status/{id}.
• Link external resources explicitly by title or domain; do not hide important blog/paper links behind the X post alone.
• Avoid over-weighting repeated posts from one author; collapse near-duplicates in prose.
• Be clear when a conclusion is inferred from post text rather than verified from the linked article.
• Keep the report concise enough for daily reading, but include enough links that the user can immediately open the primary sources.

Useful Commands

In-loop, always use the direct CLI below; never re-run this loop through the control plane (that recurses). Operators trigger the loop from a shell via its documented make/loopctl target — this skill should not.

PYTHONPATH=src python -m loopcraft.research_intel.x.cli run --config config/x_intel.yaml
PYTHONPATH=src python -m loopcraft.research_intel.x.cli snapshot-following
PYTHONPATH=src python -m loopcraft.research_intel.x.cli discover-follows --config config/x_intel.yaml
PYTHONPATH=src python -m pytest -q

Expected Output Shape

Use this structure unless the user asks for something else:

**Daily X Intelligence**
Fetched N posts; ranked M. Errors: none.

**Main Findings**
- Theme: concise synthesis. X post: [author](...). External: [resource](...). Why it matters: ...

**Frontier-Lab Highlights**
- Lab/person: summary. X post: [...](...). External: [...](...).

**Follow Candidates**
- Candidate: why they surfaced. Profile: [...](...). Evidence: [...](...).

**Loopcraft Implications**
- Actionable observation for loop engineering, harnesses, evals, agents, or tool use.