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 tmolavi/mcp-agent-skills-hub --skill github-issue-creatorgit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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/tmolavi/mcp-agent-skills-hub/github-issue-creator)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/github-issue-creator"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/github-issue-creator/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/tmolavi/mcp-agent-skills-hub/github-issue-creator"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/github-issue-creator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00036 | $0.00923 |
| Opus 5.5 | $0.00014 | $0.00369 |
| Sonnet 5.5 | $0.00007 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
github-issue-creator scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Issue Creator
Transform messy input (error logs, voice notes, screenshots) into clean, actionable GitHub issues.
Output Template
## Summary
[One-line description of the issue]
## Environment
- **Product/Service**:
- **Region/Version**:
- **Browser/OS**: (if relevant)
## Reproduction Steps
1. [Step]
2. [Step]
3. [Step]
## Expected Behavior
[What should happen]
## Actual Behavior
[What actually happens]
## Error Details
[Error message/code if applicable]
## Visual Evidence
[Reference to attached screenshots/GIFs]
## Impact
[Severity: Critical/High/Medium/Low + brief explanation]
## Additional Context
[Any other relevant details]
Output Location
Create issues as markdown files in /issues/ directory at the repo root. Use naming convention: YYYY-MM-DD-short-description.md
Guidelines
Be crisp: No fluff. Every word should add value.
Extract structure from chaos: Voice dictation and raw notes often contain the facts buried in casual language. Pull them out.
Infer missing context: If user mentions "same project" or "the dashboard", use context from conversation or memory to fill in specifics.
Placeholder sensitive data: Use [PROJECT_NAME], [USER_ID], etc. for anything that might be sensitive.
Match severity to impact:
- Critical: Service down, data loss, security issue
- High: Major feature broken, no workaround
- Medium: Feature impaired, workaround exists
- Low: Minor inconvenience, cosmetic
Image/GIF handling: Reference attachments inline. Format: !Description
Examples
Input (voice dictation):
so I was trying to deploy the agent and it just failed silently no error nothing the workflow ran but then poof gone from the list had to refresh and try again three times
Output:
## Summary
Agent deployment fails silently - no error displayed, agent disappears from list
## Environment
- **Product/Service**: Azure AI Foundry
- **Region/Version**: westus2
## Reproduction Steps
1. Navigate to agent deployment
2. Configure and deploy agent
3. Observe workflow completes
4. Check agent list
## Expected Behavior
Agent appears in list with deployment status, errors shown if deployment fails
## Actual Behavior
Agent disappears from list. No error message. Requires page refresh and retry.
## Impact
**High** - Blocks agent deployment workflow, no feedback on failure cause
## Additional Context
Required 3 retry attempts before successful deployment
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.
- 1mo ago First seen · 149 lines · 36 tokens per session scan A 1a25ffeeb457
github-issue-creator is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 923 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
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error-handling
Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.
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