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 RedHatInsights/platform-frontend-ai-toolkit --skill jira-issue-creatorgit clone --depth 1 https://github.com/RedHatInsights/platform-frontend-ai-toolkitWrote 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/redhatinsights/platform-frontend-ai-toolkit/jira-issue-creator)<a href="https://agentmods.dev/skills/redhatinsights/platform-frontend-ai-toolkit/jira-issue-creator"><img src="https://agentmods.dev/badge/skills/redhatinsights/platform-frontend-ai-toolkit/jira-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/redhatinsights/platform-frontend-ai-toolkit/jira-issue-creator"><img src="https://agentmods.dev/badge/skills/redhatinsights/platform-frontend-ai-toolkit/jira-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 | $0.00034 | $0.02073 |
| Opus 5 | $0.00017 | $0.01037 |
| Sonnet 5 | $0.00007 | $0.00415 |
| Haiku 4.5 | $0.00003 | $0.00207 |
Grade A, and why
jira-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 10d 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow
1. Parse Skill Invocation Message
Extract from message (text after skill invocation):
- summary: issue title (first sentence or main phrase)
- description: additional details for enrichment
- assignee hint: "assign to " if present
2. Resolve Assignee Hint (if present)
If "assign to bot":
user = jira_get_user_profile("712020:c6b31fa1-eaf5-4921-af5b-cb625f24bb1a")
assignee = user["email"]
assignee_type = "bot"
If "assign to me":
assignee = git config user.email
assignee_type = "user"
If "assign to ":
# Skip lookup if already email (contains '@')
if '@' in identifier:
assignee = identifier
else:
user = jira_get_user_profile(identifier) # username, display name, or account ID
assignee = user["email"]
assignee_type = "user"
If no assignee hint:
assignee = None
assignee_type = "unassigned"
3. Ask Assignee Question
Use AskUserQuestion with single question:
Question: Assignee
- header: "Assignee"
- question: "Who should this ticket be assigned to?"
- Options based on Step 2 resolution:
- If assignee resolved → "Confirm: {assignee}" (Recommended), "Unassigned", "Bot"
- If no assignee hint → "Unassigned (Recommended)", "Bot", "Assign to me"
- Other field: always available for entering username/email/display name
4. Process Assignee Answer
If "Confirm: {assignee}": Use assignee from Step 2.
If "Unassigned":
assignee = None
assignee_type = "unassigned"
If "Bot":
user = jira_get_user_profile("712020:c6b31fa1-eaf5-4921-af5b-cb625f24bb1a")
assignee = user["email"]
assignee_type = "bot"
If "Assign to me":
assignee = git config user.email
assignee_type = "user"
If Other field used (user typed custom value):
user_identifier = answers["Assignee"] # From Other field
# Resolve "me"
if user_identifier.lower() == "me":
assignee = run("git config user.email")
assignee_type = "user"
# Email (has '@')
elif '@' in user_identifier:
assignee = user_identifier
assignee_type = "user"
# Lookup
else:
user = jira_get_user_profile(user_identifier)
assignee = user["email"]
assignee_type = "user"
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 296 lines · 34 tokens per session scan A 6195339bae42
jira-issue-creator is a skill published in the GitHub repository RedHatInsights/platform-frontend-ai-toolkit (5 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 2,073 once invoked, about $0.0002 per session on Opus 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.
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