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 openshift-online/agent-control-plane --skill jira-loggit clone --depth 1 https://github.com/openshift-online/agent-control-planeWrote 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/openshift-online/agent-control-plane/jira-log)<a href="https://agentmods.dev/skills/openshift-online/agent-control-plane/jira-log"><img src="https://agentmods.dev/badge/skills/openshift-online/agent-control-plane/jira-log.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 18 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00179 | $0.03523 |
| Opus 5 | $0.00089 | $0.01761 |
| Sonnet 5 | $0.00036 | $0.00705 |
| Haiku 4.5 | $0.00018 | $0.00352 |
Grade A, and why
jira-log 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 8d 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 — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jira Issue Logger
Create well-structured Jira issues in the ENGPROD project with the acp (Agent Control Plane) component pre-filled. Every issue is built to be agent-actionable from a cold start — meaning another agent (or human) can pick it up and start working immediately without asking clarifying questions.
User Input
$ARGUMENTS
Consider the user input before proceeding (if not empty).
Recognizing Single vs Batch Mode
Single ticket (default): The input is a sentence, paragraph, or block of text describing one piece of work.
Batch mode: The input contains a markdown bullet list (lines starting with - or * ). Each top-level bullet becomes a separate ticket. Sub-bullets provide context for that ticket's description. The reason batch mode skips interactive prompting is that asking questions for each of 10+ tickets would be exhausting — instead, use sub-bullet context and reasonable defaults, then confirm the full batch before creating.
Execution
Step 1 — Parse
Extract from user input, per ticket:
| Field | Default | Notes |
|---|---|---|
| Summary | (required) | Title of the issue |
| Issue Type | Story | Also: Bug, Task, Spike, Epic. Normalize case. |
| Priority | (inferred or ask) | See priority inference below. Values: Blocker, Critical, Major, Normal, Minor. |
| Activity Type | (inferred) | See inference rules below. |
| Description | (from context) | Sub-bullets, multi-line text, or gathered interactively |
| Epic link | — | If a ticket should belong to an epic |
| Blocking | — | "X blocks Y" relationships |
| Related | — | "X related to Y" relationships |
Type prefix syntax: [Bug] Session crashes → type=Bug, summary="Session crashes".
Activity Type Inference
Infer Activity Type (customfield_10464) from the issue type and content. The user can
override by specifying it explicitly (e.g. Activity: Security & Compliance).
| Signal | Activity Type |
|---|---|
| New features, stories, product capabilities, CLI commands, UI views, API endpoints | Product / Portfolio Work |
| Bugs, alerts, stability fixes, quality improvements, rebranding, lint/format cleanup | Quality / Stability / Reliability |
| Security hardening, CVEs, compliance, image migrations, RBAC, credential handling | Security & Compliance |
| Tech debt, refactoring, long-term sustainability, dependency upgrades, code cleanup | Future Sustainability |
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.
- 8d ago First seen · 351 lines · 179 tokens per session scan A c5790abb03bb
jira-log is a skill published in the GitHub repository openshift-online/agent-control-plane (11 stars, last pushed yesterday), licensed MIT. It adds 179 tokens to every session and 3,523 once invoked, about $0.0009 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.
Other skills, from other repositories
gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
mission-control
Interact with Mission Control — AI agent orchestration dashboard. Use when registering agents, managing tasks, syncing skills, or querying agent/task status via MC APIs.
babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
project-tasks
Use this protocol to create, update, execute, or complete the structured projecttasks backlog in a Project conversation.
agent-coordination
Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.