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 agentmods add rules/kubev2v/forklift-console-plugin/ticket-workflowgit clone --depth 1 https://github.com/kubev2v/forklift-console-pluginWhat 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 | $0.00007 | $0.00922 |
| Opus 5 | $0.00003 | $0.00461 |
| Sonnet 5 | $0.00001 | $0.00184 |
| Haiku 4.5 | $0.00001 | $0.00092 |
Grade A, and why
ticket-workflow 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 2d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ticket Workflow with Agent Tracking
Invoke with: "start working on MTV-XXX", Jira ticket URL, "ticket workflow"
This workflow defines how to execute a complete Jira ticket from analysis to PR using agent tracking.
1. Workflow Steps
When a ticket URL or MTV-XXXX reference is received:
- Fetch and analyze the ticket - Understand requirements
- Present analysis and solution options - Wait for user to confirm approach
- Implement after user confirms - Follow the selected approach
- Run review agents - UX, QE, Security, Forklift Expert
- Ask to implement recommendations - After each review phase
2. Phase 1: Understand the Issue
- Use
get_jiratool to fetch the ticket details by key (e.g., MTV-1885) - Use
get_issue_commentsto read any additional context from the team - Identify key information:
- For bugs: affected version, steps to reproduce, expected vs actual behavior, logs/screenshots
- For features: user story, acceptance criteria, technical requirements, mockups
3. Phase 2: Investigate the Codebase
- Based on the ticket description, identify likely affected areas:
- Component mentioned in the ticket
- Error messages that can be searched
- UI elements or API endpoints involved
- Search the codebase for related code
- Trace the code flow that would be executed
- Identify the root cause (bugs) or implementation approach (features)
4. Phase 3: Present Analysis
Present findings to the user:
- Root Cause / Approach: Clear explanation
- Affected Files: List of files that need modification
- Solution Options: Provide 2-3 possible approaches with tradeoffs:
- Option A: [Quick fix] - Description and implications
- Option B: [Proper fix] - Description and implications
- Option C: [Comprehensive fix] - If applicable
5. Phase 4: Implementation (after user selects approach)
- Implement the selected fix/feature
- Follow project coding conventions (AGENTS.md)
- Add appropriate error handling
- Consider edge cases
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.
- 2d ago First seen · 136 lines · 7 tokens per session scan A 78fd3de389c2
ticket-workflow is a cursor rule published in the GitHub repository kubev2v/forklift-console-plugin (11 stars, last pushed 3d ago), licensed Apache-2.0. It adds 7 tokens to every session and 922 once invoked, about $0.0000 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 2026-08-30.
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