plugins/lisa/skills/plan-execute/SKILL.md
This skill should be used for any non-trivial request — features, bugs, stories, epics, spikes, or multi-step tasks. It accepts a ticket URL (Jira, Linear, GitHub), a file path containing a spec, or a plain-text prompt. It assembles an agent team, breaks the work into structured tasks, and manages the full lifecycle from research through implementation, code review, deploy, and empirical verification.
npx skillsauth add codyswanngt/lisa plan-executeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
$ARGUMENTS is either a url to a ticket containing the request, a pointer to a file containing the request or the request in text format.
If it's a ticket, use either the Jira CLI (if it's a jira ticket), the Linear CLI (if it's a linear ticket) or the Github CLI (if it's a github ticket) to read and fully understand the request, including any comments or meta data associated with the ticket.
If it's a file, read the entire file without offset or limit to understand the request.
Is this a simple request? Just execute it as usual and ignore the rest...
Otherwise:
Review all available agent types listed in the Task tool's subagent_type options. This includes built-in agents (like Explore, general-purpose), custom agents (from .claude/agents/), and plugin agents (from .claude/settings.json enabledPlugins). For each agent, explain in one sentence why it IS or IS NOT relevant to this task. Then select all agents that are relevant. You MUST justify excluding an agent — inclusion is the default.
When deciding the agents to use, consider:
metadata.relevant_documentation with the findings.NOTE: Every team must include the Explore agent
Create an agent team composed of the selected agents. Spawn every agent with mode: "plan" so they must submit their plan for team lead approval before making any file changes.
Use the TeamCreate tool to create the team before doing anything else.
Using the general-purpose agent in Team Lead session, Determine the name of this plan
Using the general-purpose agent in Team Lead session, Determine what branch to use:
Using the general-purpose agent in Team Lead session, Determine which flow applies:
If Implement, determine the work type:
Run the readiness gate check for the selected flow as defined in the intent-routing rule (loaded via the lisa plugin). If the gate fails, stop and report what is missing.
IF it is a Fix (bug), execute the Reproduce sub-flow FIRST:
Using the general-purpose agent in Team Lead session, determine how you will know that the task is fully complete
Using the general-purpose agent in Team Lead session, create tasks needed to complete the request.
Every task MUST include this JSON metadata block. Do NOT omit skills (use [] if none), learnings (use [] if none) or verification.
{
"plan": "<plan-name>",
"type": "spike|bug|task|epic|story",
"acceptance_criteria": ["..."],
"relevant_documentation": "",
"testing_requirements": ["..."],
"skills": ["..."],
"learnings": ["..."],
"verification": {
"type": "ui-recording|api-test|cli-test|database-check|manual-check|documentation",
"command": "the proof command — must run the actual system (NOT test/typecheck/lint, those are quality gates)",
"expected": "what success looks like — observable system behavior"
}
}
Before any task is implemented, the agent team must explore the codebase for relevant research (documentation, code, git history, etc) and update each task's metadata.relevant_documentation with the findings.
Each task must be reviewed by the team to make sure their verification passes. Each task must have their learnings reviewed by the learner subagent.
Before shutting down the team, execute the Verify flow:
verification-specialist: verify locally by running the actual system and observing results (empirical proof that the change works). This is the real verification step.verification-specialist verifies in target environment (same checks as local verification, but on remote)ops-specialist: post-deploy health check, monitor for errors in first minutestools
--- name: harper-realtime description: This skill should be used when adding or troubleshooting Harper (HarperDB/Fabric) real-time behavior: MQTT topics, WebSocket resource subscriptions, resource publish/subscribe handlers, SSE-style streaming routes, and local subscriber verification. Pairs with harper-resources, harper-config-yaml, harper-schema-graphql, and harper-build-and-deploy. --- # Harper Realtime ## Overview Harper exposes live data through the same Resource model used for REST and
tools
--- name: harper-realtime description: This skill should be used when adding or troubleshooting Harper (HarperDB/Fabric) real-time behavior: MQTT topics, WebSocket resource subscriptions, resource publish/subscribe handlers, SSE-style streaming routes, and local subscriber verification. Pairs with harper-resources, harper-config-yaml, harper-schema-graphql, and harper-build-and-deploy. --- # Harper Realtime ## Overview Harper exposes live data through the same Resource model used for REST and
tools
--- name: harper-realtime description: This skill should be used when adding or troubleshooting Harper (HarperDB/Fabric) real-time behavior: MQTT topics, WebSocket resource subscriptions, resource publish/subscribe handlers, SSE-style streaming routes, and local subscriber verification. Pairs with harper-resources, harper-config-yaml, harper-schema-graphql, and harper-build-and-deploy. --- # Harper Realtime ## Overview Harper exposes live data through the same Resource model used for REST and
tools
--- name: harper-realtime description: This skill should be used when adding or troubleshooting Harper (HarperDB/Fabric) real-time behavior: MQTT topics, WebSocket resource subscriptions, resource publish/subscribe handlers, SSE-style streaming routes, and local subscriber verification. Pairs with harper-resources, harper-config-yaml, harper-schema-graphql, and harper-build-and-deploy. --- # Harper Realtime ## Overview Harper exposes live data through the same Resource model used for REST and