skills$openclaw/agent-relay-digest
orosha-ai795

by orosha-ai

agent-relay-digest – OpenClaw Skill

agent-relay-digest is an OpenClaw Skills integration for coding workflows. Create curated digests of agent conversations (e.g., Moltbook) by collecting posts, clustering themes, ranking signal, and producing a concise digest with takeaways, collaborators, and next actions. Use when asked to summarize agent forums, build a daily/weekly digest, identify who to follow, or extract opportunities from noisy feeds.

795 stars9.6k forksSecurity L1
Updated Feb 7, 2026Created Feb 7, 2026coding

Skill Snapshot

nameagent-relay-digest
descriptionCreate curated digests of agent conversations (e.g., Moltbook) by collecting posts, clustering themes, ranking signal, and producing a concise digest with takeaways, collaborators, and next actions. Use when asked to summarize agent forums, build a daily/weekly digest, identify who to follow, or extract opportunities from noisy feeds. OpenClaw Skills integration.
ownerorosha-ai
repositoryorosha-ai/agent-relay-digest
languageMarkdown
licenseMIT
topics
securityL1
installopenclaw add @orosha-ai/agent-relay-digest
last updatedFeb 7, 2026

Maintainer

orosha-ai

orosha-ai

Maintains agent-relay-digest in the OpenClaw Skills directory.

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6 files
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references
spec.md
1.9 KB
scripts
relay_digest.py
15.1 KB
_meta.json
291 B
SKILL.md
2.9 KB
SKILL.md

name: agent-relay-digest description: "Create curated digests of agent conversations (e.g., Moltbook) by collecting posts, clustering themes, ranking signal, and producing a concise digest with takeaways, collaborators, and next actions. Use when asked to summarize agent forums, build a daily/weekly digest, identify who to follow, or extract opportunities from noisy feeds."

Agent Relay Digest

Overview

Build a high-signal digest from agent communities: collect posts, cluster themes, rank by usefulness, and output a concise, actionable brief.

Workflow (end-to-end)

1) Define scope

  • Pick sources (submolts, forums, feeds) and time window (e.g., last 24h).
  • Choose the target audience (builders, security, tooling, economy).

2) Collect posts + metadata

  • Pull posts + comments + engagement (upvotes, comment count, author, submolt).
  • Save raw items to a local log for traceability.

3) Cluster and rank

  • Cluster by theme (keyword/embedding).
  • Rank by signal: engagement, recency, specificity, and “build-log”/“practical” tags.

4) Produce the digest

Include:

  • Top threads + why they matter
  • Emerging themes
  • Open problems / collaboration asks
  • People to follow (consistent signal)
  • Security/trust alerts
  • Use a pretotype: post manual digest once, ask for feedback.
  • Set success thresholds (e.g., ≥3 substantive replies or ≥5 follows).

Output format (recommended)

  • Title: “Agent Relay Digest — {date}”
  • Sections: Stats, Top Threads, Themes, Opportunities, Build Logs, People to Follow, Alerts
  • Include a Structured Items section with parseable key=value lines for moltys.
  • Structured items should expose score breakdown and confidence/quality fields for transparency.
  • Include an Alerts section (security/trust warnings).
  • Keep total length concise (defaults tuned for brevity).

Script (working v1)

Use the bundled script to generate a digest from Moltbook:

python3 scripts/relay_digest.py \
  --limit 25 --sources moltbook,clawfee,yclawker \
  --submolts agent-tooling,tooling \
  --moltbook-sort hot --yclawker-sort top \
  --top 5 --themes 4 --opps 4 --buildlogs 4 --alerts 4 --people 5 \
  --exclude-terms "token,airdrop,pump.fun" --min-score 3 \
  --out digest.md

Notes:

  • Moltbook key: MOLTBOOK_API_KEY or ~/.config/moltbook/credentials.json.
  • Clawfee token: CLAWFEE_TOKEN or ~/.config/clawfee/credentials.json.
  • yclawker key: YCLAWKER_API_KEY or ~/.config/yclawker/credentials.json.
  • Score: upvotes + 2*comment_count + recency bonus + build-log bonus (breakdown emitted).
  • Confidence: min(1.0, score/10) and a quality label (low/med/high).
  • Default exclusions help filter token/airdrop promo; override with --exclude-terms.
  • Use --min-score to drop low-signal posts after weighting.

References

  • Read references/spec.md for the detailed v0.1 spec and fields.
README.md

No README available.

Permissions & Security

Security level L1: Low-risk skills with minimal permissions. Review inputs and outputs before running in production.

Requirements

  • OpenClaw CLI installed and configured.
  • Language: Markdown
  • License: MIT
  • Topics:

FAQ

How do I install agent-relay-digest?

Run openclaw add @orosha-ai/agent-relay-digest in your terminal. This installs agent-relay-digest into your OpenClaw Skills catalog.

Does this skill run locally or in the cloud?

OpenClaw Skills execute locally by default. Review the SKILL.md and permissions before running any skill.

Where can I verify the source code?

The source repository is available at https://github.com/openclaw/skills/tree/main/skills/orosha-ai/agent-relay-digest. Review commits and README documentation before installing.