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.
git clone --depth 1 https://github.com/notorious-ai/claude-pluginsWrote 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/commands/notorious-ai/claude-plugins/draft-issue)<a href="https://agentmods.dev/commands/notorious-ai/claude-plugins/draft-issue"><img src="https://agentmods.dev/badge/commands/notorious-ai/claude-plugins/draft-issue/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/commands/notorious-ai/claude-plugins/draft-issue"><img src="https://agentmods.dev/badge/commands/notorious-ai/claude-plugins/draft-issue.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 | $0.00011 | $0.01111 |
| Opus 5.5 | $0.00004 | $0.00444 |
| Sonnet 5.5 | $0.00002 | $0.00222 |
| Haiku 4.5 | $0.00001 | $0.00111 |
Grade A, and why
draft-issue 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft GitHub Issue
Guide the user through creating an effective issue using the writing-issues skill. An issue lives entirely in the problem space. The interview gathers the problem's why, how, and what, then derives validation criteria from that context rather than from a guess about the solution.
Step 1: Load Conventions and Check for Issue Templates
Invoke the writing-issues skill before asking anything. The conventions govern every question below, so they load first.
Look for repository issue templates:
!ls -la .github/ISSUE_TEMPLATE/ 2>/dev/null || echo "No issue templates found"
If templates exist, list them and ask user if they want to follow a template.
Step 2: Determine Issue Type
Use AskUserQuestion:
- question: "What type of issue are you creating?"
- header: "Issue type"
- options:
- Bug report (Something isn't working correctly)
- Feature request (A new capability or enhancement)
- Task tracking (Work that needs to be done)
- Discussion (Exploring a decision or gathering input)
Step 3: Surface the Hidden Context
Probe for three distinct kinds of context. All three come from the problem space, and the writing-issues skill governs how each is written.
- The friction and its cost - what breaks down or goes unmet, who bears it, and what it costs to leave it alone
- The constraints - what any solution must live within: deadlines, operational limits, dependencies, and attempts that already failed. Put each one through the skill's constraint test before recording it.
- The observable symptoms - the visible pain that shows the problem is real: metrics, reproductions, incident records, complaint volumes. Record the numbers the user actually has, and none they do not.
Ask type-specific questions:
Bug: What were you trying to accomplish, and what does the failure cost you? What should have happened, and what happened instead? How reproducible is it, and how often does it occur? Does anything constrain a fix, such as supported versions or data that cannot be migrated?
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 · 94 lines · 11 tokens per session scan A 87cc9d042bef
draft-issue is a command published in the GitHub repository notorious-ai/claude-plugins (5 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 1,111 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
template
Manage issue templates for streamlined issue creation.
fest-show
Show festival progression (in-progress tasks, roadmap, and dependency view).
add-note
Add an internal or external note to a ConnectWise PSA ticket.
sync-linear
Sync current work with Linear ticket status.
dispatcher
Use when you want the orchestrator to pick the next repo to work on across your whole portfolio — it enumerates candidate repos below the confinement root, resolves free/busy from each repo's session.lock lease, ranks the FREE ones by backlog priority × staleness × readiness, recommends the single most worthwhile one…
workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.