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 faberlens/hardened-skills --skill jira-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-skillsWrote 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/faberlens/hardened-skills/jira-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/jira-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/jira-hardened/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/faberlens/hardened-skills/jira-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/jira-hardened.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 | $0.00070 | $0.01908 |
| Opus 5 | $0.00035 | $0.00954 |
| Sonnet 5 | $0.00014 | $0.00382 |
| Haiku 4.5 | $0.00007 | $0.00191 |
Grade A, and why
jira-hardened scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
"description": "Needed for REST/curl fallback; not required for jira CLI or MCP backends", This is a copy
91% identical to jira — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jira
Natural language interaction with Jira. Supports multiple backends.
Backend Detection
Run this check first to determine which backend to use:
1. Check if jira CLI is available:
→ Run: which jira
→ If found: USE CLI BACKEND
2. If no CLI, check for Atlassian MCP:
→ Look for mcp__atlassian__* tools
→ If available: USE MCP BACKEND
3. If neither available:
→ GUIDE USER TO SETUP
| Backend | When to Use | Reference |
|---|---|---|
| CLI | jira command available |
references/commands.md |
| MCP | Atlassian MCP tools available | references/mcp.md |
| None | Neither available | Guide to install CLI |
Quick Reference (CLI)
Skip this section if using MCP backend.
| Intent | Command |
|---|---|
| View issue | jira issue view ISSUE-KEY |
| List my issues | jira issue list -a$(jira me) |
| My in-progress | jira issue list -a$(jira me) -s"In Progress" |
| Create issue | jira issue create -tType -s"Summary" -b"Description" |
| Move/transition | jira issue move ISSUE-KEY "State" |
| Assign to me | jira issue assign ISSUE-KEY $(jira me) |
| Unassign | jira issue assign ISSUE-KEY x |
| Add comment | jira issue comment add ISSUE-KEY -b"Comment text" |
| Open in browser | jira open ISSUE-KEY |
| Current sprint | jira sprint list --state active |
| Who am I | jira me |
Quick Reference (MCP)
Skip this section if using CLI backend.
| Intent | MCP Tool |
|---|---|
| Search issues | mcp__atlassian__searchJiraIssuesUsingJql |
| View issue | mcp__atlassian__getJiraIssue |
| Create issue | mcp__atlassian__createJiraIssue |
| Update issue | mcp__atlassian__editJiraIssue |
| Get transitions | mcp__atlassian__getTransitionsForJiraIssue |
| Transition | mcp__atlassian__transitionJiraIssue |
| Add comment | mcp__atlassian__addCommentToJiraIssue |
| User lookup | mcp__atlassian__lookupJiraAccountId |
| List projects | mcp__atlassian__getVisibleJiraProjects |
What ships with it
1 file 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.
- 9d ago First seen · 232 lines · 70 tokens per session scan A 029e19868006
jira-hardened is a skill published in the GitHub repository faberlens/hardened-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 1,908 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to jira, differing in 38 lines, and is treated as a copy.
Other skills, from other repositories
risk-metrics-calculation
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
employment-contract-templates
Create employment contracts, offer letters, and HR policy documents following legal best practices. Use when drafting employment agreements, creating HR policies, or standardizing employment documentation.
calendar
Calendar and scheduling management. Use this skill when the user needs to create, view, update, or manage calendar events, appointments, meetings, or schedule-related tasks. Supports ICS file format, recurring events, and timezone handling.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
google-calendar-skill
Manage Google Calendar - search, create, update events and answer calendar questions. Use when user wants to interact with their Google Calendar for scheduling and calendar operations.
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.