Install
$ agentstack add skill-oyi77-1ai-skills-clawild-moltbook ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Clawild Moltbook
When to Use
Trigger phrases:
- "clawild moltbook"
- "Interacting with Moltbook for crypto intelligence"
- "When user wants to engage with CLAWILD agent"
- "For crypto narrative detection tasks"
- 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 automates workflow automation to reduce manual effort and increase reliability.
Workflow
# Example: Workflow automation
import schedule
import time
def run_workflow():
data = fetch_data()
processed = transform(data)
deliver(processed)
schedule.every().hour.do(run_workflow)
while True:
schedule.run_pending()
time.sleep(60)
- Define triggers — Set up events or schedules that initiate the automation
- Configure inputs — Specify data sources and parameters
- Design pipeline — Define the sequence of automated steps
- Add error handling — Set up retries, alerts, and fallback paths
- Test end-to-end — Validate the full automation with realistic data
- Deploy and monitor — Activate and track performance
Configuration
- Set trigger conditions (schedule, webhook, event)
- Define input validation rules
- Configure notification channels for alerts
- Set retry policies and timeout limits
Best Practices
- Start with simple automations and iterate
- Add logging at every step for debugging
- Use idempotent operations where possible
- Test with edge cases before deploying
Anti-Rationalization
| Rationalization | Reality | |---|---| | "Manual is faster for one-off tasks" | One-off tasks become recurring. Automate early, save time later. | | "I will add error handling later" | You never do. Handle errors from day one. | | "Automation is overkill" | If you do it twice, automate it. If you do it daily, it is critical infrastructure. |
Process
- Prepare — Gather requirements, verify prerequisites, set up environment
- Execute — Run clawild moltbook workflow with configured parameters
- Verify — Validate output meets requirements, document results
Verification
- [ ] All steps executed successfully
- [ ] Results validated against acceptance criteria
- [ ] Error handling tested with edge cases
- [ ] Documentation updated with findings
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: oyi77
- Source: oyi77/1ai-skills
- License: MIT
- Homepage: https://oyi77.github.io/1ai-skills
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.