yeaboi.ai
Official@yeaboi-ai · United Kingdom
yeaboi.ai provides engineering-management software for sprint planning, standups, delivery reporting, performance reviews, and AI coding-agent cost and security auditing.
Agent Skills by yeaboi.ai
Showing 21 vetted skills indexed across 1 GitHub repositories.
yeaboi-aiteam-analysis
Analyzes Jira and Azure DevOps history into team velocity, estimation, and AI-adoption profiles.
yeaboi-aiagents-advisor
Analyzes local AI-agent session logs to estimate recoverable spend and prompt-cache health.
yeaboi-aiceremonies
Inspect scheduled yeaboi ceremonies, their cadences, delivery channels, and run outcomes.
yeaboi-aidelivery-report
Generate stakeholder-ready delivery reports from completed Jira or Azure DevOps tickets.
yeaboi-aiweekly-review
Generates a solo developer weekly self-review from standups, delivered work, and sprint plans.
yeaboi-ainiko
Answers cross-mode questions about yeaboi planning, standups, retros, and agent costs.
yeaboi-aistandup
Generates daily scrum standup reports from ticketing, code, and documentation activity.
yeaboi-aiprovenance
Audit hash-chained decision logs and trace evidence behind yeaboi's automated signals.
yeaboi-aiagents-usage
Generates per-model, per-project AI agent cost reports from local Claude Code session logs.
yeaboi-aiplan-sprint
Generates epics, user stories, tasks, and sprint plans through a conversational intake with the yeaboi MCP server.
yeaboi-aiagents-security
Audit local AI-agent configurations and session transcripts for risky permissions, secrets, and commands.
yeaboi-aiship
Inspects yeaboi ship run history, statuses, costs, and batched PR progress.
yeaboi-aiperformance
Generates 1:1 preps, summaries, and performance reviews from Jira and Azure DevOps delivery data.
yeaboi-aislack-inbound
Inspect Slack reactions and thread replies applied to yeaboi ceremonies and practice signals.
yeaboi-aitui-standards
Enforces shared component standards for building terminal UI screens in Python.
yeaboi-aimode-blueprints
Documents internal architecture blueprints for yeaboi's standup, retro, performance, and reporting modes.
yeaboi-aici-and-release
Documents CI/CD workflows, version bumping, and PyPI release mechanics for GitHub Actions.
yeaboi-aiagent-and-state
Defines conventions for LangGraph agent nodes, prompts, tools, LLM providers, and state serialization.
yeaboi-aiproject-map
Maps the yeaboi codebase modules, CLI flags, environment variables, and MCP server internals.
yeaboi-ailogging
Configures centralized logging handlers, per-mode log routing, and session logs for the yeaboi TUI.
yeaboi-aiweb-frontend
Enforces browser-surface conventions for Python-to-TypeScript web asset boundaries and wire contracts.
Frequently Asked Questions About yeaboi.ai
FAQPage SchemaWhat tasks can I accomplish with yeaboi.ai's skills?▼
You can plan projects into epics, stories, and sprints; run daily standups with sprint-confidence scoring; generate stakeholder delivery reports; analyze team velocity and estimation accuracy from Jira/Azure DevOps; prepare 1:1s and performance reviews; and audit AI coding-agent token costs, cache efficiency, and security settings.
Who is yeaboi.ai designed for?▼
It targets engineering managers, scrum masters, and tech leads running agile teams, plus solo developers wanting weekly self-reviews. Platform and security engineers benefit from the agentwatch family, which audits local AI coding-agent spend, prompt caching, permissions, and MCP server risks from session logs.
How does yeaboi.ai handle AI coding-agent cost and security auditing?▼
The agents-usage and agents-advisor skills compute per-model, per-project token costs and identify recoverable spend from re-reads and cache-death gaps, all from local session logs. The agents-security skill scans permission bypasses, wildcard rules, MCP servers, and secrets, offering verdicts, session replays, and one-click fixes.
How does yeaboi.ai ensure its automated decisions are auditable?▼
The provenance skill records every practice nudge, blocker flag, confidence adjustment, conflict card, and performance prep in a tamper-evident, hash-chained local log with its evidence. Users can trace why any signal was raised and verify the decision history has not been altered, supporting compliance audits.
What data sources and integrations does yeaboi.ai require?▼
yeaboi reads Jira/Azure DevOps history for team calibration, ticketing/code/documentation activity for standups, local Claude Code session logs for agent cost and security analysis, Slack reactions and thread replies via its inbound reader, and supervised plan-item-to-PR runs through its ship mode with human approval gates.