Simulate human stakeholder responses in STI dry-run ticket workflows.
npx skills add https://github.com/gapfDev/workflow --skill dryrun-human
Please help me install this Agent Skill. Skill: dryrun-human Source: https://github.com/gapfDev/workflow/tree/main/.claude/skills/dryrun-human Command: npx skills add https://github.com/gapfDev/workflow --skill dryrun-human
The Dry Run — Human Agent provides a realistic human stakeholder persona to participate in STI dry-run tests, enabling accurate decision-making and validation without exposing real agents.
Review the synthetic ticket at temp/dryrun/{V}/[ticket-id]/synthetic-ticket.md and start recording your human responses in temp/dryrun/{V}/[ticket-id]/human-responses.md.
High-intent search queries and answers about installing and using this skill.
To simulate a human stakeholder for an STI dry-run workflow, this Skill provides realistic ticket input and decision feedback at S1, S2, S3, and S4 stopper points without exposing real agents. It enforces STOPPER workflow rules to validate decisions accurately.
A stopper decision point in dry-run ticket workflows is a stage requiring human input or approval, such as S1 input, S2 intake, or S3/S4 flow. This Skill enforces these rules and generates human responses to validate the process.
To record human responses during a dry-run test, the Skill writes exclusively to the temp/dryrun/{V}/[ticket-id]/human-responses.md file. It reads allowed input from synthetic-ticket.md and pending-stopper.md to generate accurate stakeholder feedback.
Yes, you can use this for STI dry runs without exposing real agents. It simulates a realistic human stakeholder who files and reviews tickets, providing necessary decision feedback while keeping actual agents safe during validation.
No, the dry-run human agent does not read from any context source available. It strictly reads only from allowed input files like synthetic-ticket.md and pending-stopper.md located in the temp/dryrun directory structure.
A dry-run workflow needs a simulated human stakeholder to enable accurate decision-making and validation at stopper points. It provides realistic ticket input and feedback, ensuring the workflow functions correctly before involving actual human agents.
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