Borrowing it
Nothing to install: this file belongs to mckinsey/agents-at-scale-ark. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mckinsey/agents-at-scale-ark/main/.claude/skills/issue-creation/SKILL.mdgit clone --depth 1 https://github.com/mckinsey/agents-at-scale-arkWrote 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/mckinsey/agents-at-scale-ark/issue-creation)<a href="https://agentmods.dev/skills/mckinsey/agents-at-scale-ark/issue-creation"><img src="https://agentmods.dev/badge/skills/mckinsey/agents-at-scale-ark/issue-creation/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/mckinsey/agents-at-scale-ark/issue-creation"><img src="https://agentmods.dev/badge/skills/mckinsey/agents-at-scale-ark/issue-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01398 |
| Opus 5.5 | $0.00023 | $0.00559 |
| Sonnet 5.5 | $0.00012 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
issue-creation 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 2mo 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Creation
Create well-researched, problem-focused GitHub issues for mckinsey/agents-at-scale-ark.
When to use this skill
Use when the user asks to create an issue, report a bug, request a feature, or track work. This skill enforces a research-first approach before creating any issue.
Process
Follow these steps in order. Do NOT skip steps.
Step 1: Ask clarifying questions
ALWAYS ask clarifying questions before doing any research or drafting. Never assume you have enough context. Use AskUserQuestion to gather:
- What exactly is the problem? (Get specifics, not just a vague description)
- Who is affected and how severely?
- How is the problem reproduced? (for bugs)
- What is the motivation or trigger? (for features)
- Is there any urgency or deadline?
- Are there any constraints the user already knows about?
Do NOT proceed to Step 2 until the user has answered your questions. If the user's initial description is detailed, still confirm your understanding by summarizing back and asking if anything is missing.
Step 2: Research the codebase
Before writing anything, investigate the relevant code:
- Find the files, modules, and components involved
- Understand the current behavior and architecture
- Identify the scope and blast radius of the problem
- Note relevant file paths and code references
Use Grep, Glob, Read, and the Explore agent as needed. Include key findings in the issue's Context section so reviewers can orient themselves without re-doing the research.
Important: The purpose of research is to understand the problem's scope and surface area — NOT to prescribe a solution. Do not let research findings leak into prescriptive implementation steps. Knowing which files are involved helps the implementer orient; telling them what to change in those files anchors them on a path that may be wrong.
Step 3: Check for duplicates and dependencies
Search existing issues thoroughly in the main repo:
gh search issues --repo mckinsey/agents-at-scale-ark "<keywords>" --json number,title,state,labels --jq '.[] | "\(.number) [\(.state)] \(.title)"'
gh issue list --repo mckinsey/agents-at-scale-ark --state open --json number,title,labels --jq '.[] | "\(.number) \(.title)"' | grep -i "<keyword>"
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.
- 2mo ago First seen · 129 lines · 58 tokens per session scan A d4610ea00fb5
issue-creation is a skill published in the GitHub repository mckinsey/agents-at-scale-ark (434 stars, last pushed yesterday), licensed Apache-2.0. It adds 58 tokens to every session and 1,398 once invoked, about $0.0002 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.
issue-creation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| issue-creationmckinsey/agents-at-scale-ark | Skill | 434 | yesterday | Apache-2.0 | A | 58 | Claude Code |
| container-manager-kubernetes-operationsKnuckles-Team/container-manager-mcp | Skill | 6 | yesterday | MIT | A | 189 | Claude Code, Codex |
| kubernetes-mesh-provisionerKnuckles-Team/container-manager-mcp | Skill | 6 | yesterday | MIT | C | 60 | Claude Code, Codex |
| k8s-opsjingyanjiang/k8s-mcp | Skill | 3 | 5mo ago | MIT | A | 77 | Claude Code |
| linkerd-patternswshobson/agents | Skill | 42k | yesterday | MIT | A | 41 | Claude Code |
| cost-optimizationwshobson/agents | Skill | 42k | yesterday | MIT | A | 48 | Claude Code |
| istio-traffic-managementwshobson/agents | Skill | 42k | yesterday | MIT | A | 40 | Claude Code |