agentify-project
Make a project ready for AI agentic engineering by converging it toward a canonical agent-neutral structure — a lean AGENTS.md index with progressive…
Verify the engineer is ready to implement a feature — a teach-back conversation over a .TICKET-REVIEW.md where they explain the feature in their own words and the agent probes and corrects.
$ npx -y skills add eai-org/agent-toolkit --skill verify-understanding --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/verify-understandingContext preview
The summary Claude sees to decide when to auto-load this skill.
Verify the engineer is ready to implement a feature — a teach-back conversation over a .TICKET-REVIEW.md where they explain the feature in their own words and the agent probes and corrects.
name: verify-understanding description: Verify the engineer is ready to implement a feature — a teach-back conversation over a .TICKET-REVIEW.md where they explain the feature in their own words and the agent probes and corrects. disable-model-invocation: true type: flow license: MIT metadata: version: "1.1"
One goal, nothing more, nothing less: verify the engineer is ready to move to the implementation phase. Exercise their understanding of a feature before they work on it — the pipeline's other artifacts serve fresh agent sessions; this step serves the human. Active recall: the user produces the feature in their own words, you only steer and fix. Re-runnable: right after the ticket review, or days later just before implementation — every run starts fresh from the review file, no state kept between runs.
A `.TICKET-REVIEW.md`. Read it and the ticket files and parent it links. A reference that exists only as a tracker URL (no local file) may be fetched read-only at conversation time.
A conversation, not a test — the last sync between a dev and their PO before development starts. Word your probes and corrections via `explain-in-simple-language` when available.
1. Ask the user to explain the feature in their own words: who uses it, what they see and do, why the feature exists, and what each ticket contributes. 2. Let them produce the narrative. Probe one weak spot at a time; correct and complete only from the review file and the referenced materials — flag what those don't settle, never improvise requirements. 3. Stay at that PO–dev depth: journey, actors, purpose, per-ticket contribution — not DTO, field, or architecture detail.
When the product's user-facing language differs from the conversation's, name each page, text, or element with its user-facing term in parentheses — "the client area (klantportaal)".
Done when the user has correctly produced — corrections absorbed — the journey, why the feature exists, and every input ticket's contribution: enough to start implementing. Close with a short wrap-up naming anything that stayed shaky, then point at `/refine-ticket <ticket-file>` per ticket, in the set's suggested execution order.
A collection of generic agentic tools for common engineering tasks, designed to work with any AI agent on any kind of software project.
Make a project ready for AI agentic engineering by converging it toward a canonical agent-neutral structure — a lean AGENTS.md index with progressive…
Check how much of a ticket is already implemented — split it into requirement blocks, judge each against the code, and save a human-readable TICKET-STATUS…
Draft, rewrite, or refine a doc for maximum token economy without losing any rule or intent. Use for docs kept in version control and regularly re-read by…
Author or refine a skill for maximum token economy without losing intent. Use when creating any new skill or editing an existing `SKILL.md`.
Audit what auto-loads into an agent session's context window and suggest lean, reversible fixes to cut startup tokens.
Turn a refined requirements document into a structured implementation PLAN.md a fresh session can execute. Planning only — decides the "how", not the "what".…