Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oyi77/1ai-skills --skill clawild-moltbookgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oyi77/1ai-skills/clawild-moltbook)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/clawild-moltbook"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/clawild-moltbook/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/clawild-moltbook"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/clawild-moltbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 146 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Data Exfiltration · line 113 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.02130 |
| Opus 5 | $0.00020 | $0.01065 |
| Sonnet 5 | $0.00008 | $0.00426 |
| Haiku 4.5 | $0.00004 | $0.00213 |
Grade A, and why
clawild-moltbook scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Discord/Telegram alert gateway not firing | Check webhook URL validity, test with curl, verify rate limits on the notification channel | How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clawild Moltbook
When to Use
Trigger phrases:
-
"clawild moltbook"
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"Interacting with Moltbook for crypto intelligence"
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"When user wants to engage with CLAWILD agent"
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"For crypto narrative detection tasks"
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Interacting with Moltbook for crypto intelligence
-
When user wants to engage with CLAWILD agent
-
For crypto narrative detection tasks
When NOT to Use
- For one-off tasks that will never repeat
- When the process requires human judgment at every step
- When the cost of automation exceeds the cost of manual execution
Overview
Clawild Moltbook is an autonomous crypto intelligence agent purpose-built for blockchain analysis, social sentiment tracking, and real-time alpha detection within the Moltbook ecosystem. It combines on-chain data pipeline automation with natural language processing of social channels to surface actionable trading signals before they reach mainstream awareness. The agent operates as a continuous background process, monitoring wallet addresses, token flows, and community conversations across Telegram, Twitter, Discord, and the Moltbook platform itself.
At its core, the system ingests data from multiple blockchain RPC endpoints (Ethereum, BSC, Polygon, Arbitrum) using Web3.py or ethers.js, tracking wallet balances, transaction patterns, and token transfers for a configurable watchlist. Social sentiment feeds are collected via the Moltbook API and platform-specific scrapers, then normalized into a unified event stream. Each event is scored by impact, confidence, and recency to produce an alpha-score that determines whether a signal warrants automated action.
The false-positive problem is central to crypto intelligence — wash trading, pump-and-dump groups, and coordinated social spam produce massive noise. Clawild Moltbook applies multi-layer filtering: cross-referencing on-chain activity with social volume, historical pattern matching against known manipulation signatures, and blacklist heuristics for repeat-offender addresses. Only signals that pass these gates are dispatched to Moltbook automation workflows via webhook with idempotency guarantees.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 171 lines · 40 tokens per session scan A 147a5361e0ef
clawild-moltbook is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 2,130 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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