codyswanngt/implement

Implement: $ARGUMENTS

Orchestration: agent team

Implement is a team-first flow. Bug, Build, Improve, and Investigate-Only all compose multiple specialists (Reproduce → debug → fix → review → verify). Single-agent mode is not permitted based on task complexity — the only exception is when no team creation or subagent delegation tool is available in the current runtime (see no-team fallback in the paragraph below).

If you are NOT already operating inside an agent team (no prior successful team-creation or subagent-delegation tool call in this session, not spawned into a team context), the very first thing you do is establish team orchestration.

Use the team tool for the current runtime:

• Claude: use TeamCreate. If TeamCreate has not been loaded yet, first use ToolSearch with query: "select:TeamCreate" to load its schema.
• Codex: do not call TeamCreate; Codex does not expose that Claude tool. Use tool_search with a query like multi-agent tools to load multi_agent_v1, then use multi_agent_v1.spawn_agent for teammate delegation. Treat the first successful spawn_agent call as establishing team orchestration.
• Other runtimes: use the current runtime's tool-discovery mechanism to discover and call the appropriate multi-agent/team tool.

If no team creation or subagent delegation tool is available, explicitly state that team orchestration is unavailable in this runtime, continue as the lead agent, and preserve the workflow's review, verification, and task-tracking obligations locally.

Until the team is established, the first Codex teammate has been spawned, or the no-team fallback has been declared, do NOT call any of: Agent, TaskCreate, Skill (including lisa:tracker-read, lisa:jira-read-ticket, lisa:github-read-issue), MCP tools (Atlassian / Linear / GitHub / Notion), Read, Write, Edit, Bash, Grep, Glob. Reading the ticket, exploring the code, fetching context — every one of those is a task for the team you are about to create, not for the lead session before orchestration exists. Doing them inline is the exact bypass path that produces a 1-agent ad-hoc fix instead of a real team flow.

If you ARE already inside an agent team (e.g., a teammate invoked this skill via the Skill tool, or lisa:intake is running this skill per Ready ticket), do NOT create a second team — many harnesses reject double-creates — and do NOT collapse the nested flow into a single inline worker. A nested team-first flow must still bring in the specialists it requires by adding them to the existing team, not by doing the work itself:

• Claude: teams are flat and only the lead can add named teammates, so do NOT call Agent with a name from a teammate (the harness rejects it: "Teammates cannot spawn other teammates — the team roster is flat"). Send the team lead a message naming the specialist teammate(s) this flow needs, their task assignments, and completion criteria, then coordinate through the shared task list until they finish. An anonymous subagent (Agent with name omitted) is permitted only for bounded one-shot work whose result returns directly to you — it is not a substitute for the required lifecycle specialists.
• Codex: do NOT call TeamCreate. If the lead/root agent is addressable (you were given its id/handle), send it a request to multi_agent_v1.spawn_agent the specialist agent(s), including each agent's prompt, ownership, and expected result. If no lead handle exists but spawn_agent is available to you, spawn only the bounded specialist agent(s) this flow needs, wait_agent for their results, and relay those results upward to the parent/lead.

Treat the first successful lead-spawn request (or, on the Codex fallback, the first specialist spawn) as preserving team orchestration. Never satisfy a team-first lifecycle flow by doing all the work inline.

Resolve the input (first task assigned to the team)

$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.

The team lead does NOT read the input directly. The first task on the team's plan is "resolve the input" — assigned to a bounded input-resolver teammate, which then:

• If it's a ticket, calls lisa:tracker-read (preferred — vendor-agnostic; dispatches per .lisa.config.json tracker). Mismatch guard: if the ticket format doesn't match the configured tracker (e.g., a GitHub URL when tracker is jira), tracker-read stops and reports the error — never auto-translates vendors:
  • JIRA ticket → lisa:tracker-read → lisa:jira-read-ticket
  • GitHub Issue → lisa:tracker-read → lisa:github-read-issue
  • Linear identifier or project URL → lisa:tracker-read → lisa:linear-read-issue
  • Captures comments and metadata, not just the description.
• If it's a file, reads the entire file without offset or limit.
• If it's a plain-text request, uses the provided text verbatim as the resolved input.
• Returns the resolved input to the team lead, who then proceeds to roster selection.

The input resolver is the only teammate that may be spawned before the Roster Decision exists. After it returns the resolved input, do not spawn any lifecycle, research, implementation, review, verification, or learning teammate until the Roster Decision has been recorded.

Select the agent roster

Before spawning any teammate beyond the bounded input resolver, record a Roster Decision artifact. It must enumerate every agent or specialist type exposed by the current runtime's delegation tool and record one line per type:

INCLUDE|EXCLUDE - <agent type> - <one-sentence reason>

Review all available agent types listed in the current runtime's delegation options. In Claude, this includes the Task tool's subagent_type options: built-in agents such as Explore and general-purpose, custom agents from .claude/agents/, and plugin agents from enabled plugins. In Codex, Cursor, Copilot, agy, OpenCode, or another runtime, use that runtime's tool-discovery and delegation surfaces to enumerate the equivalent available specialists. If the runtime exposes no specialist list, record that explicitly in the Roster Decision and justify the fallback agent type you will use.

Persist the Roster Decision where the flow can be audited later. Prefer task-list metadata metadata.roster when a task list exists; otherwise write ${LISA_PROJECT_DIR:-${CLAUDE_PROJECT_DIR:-.}}/.lisa/roster.md or post the Roster Decision in the plan/tracker artifact the flow is already updating. The later verification/evidence step must reference the recorded artifact; absence of the artifact is a workflow failure.

Inclusion is the default. You MUST justify excluding an agent. Every team must include the Explore agent, or the runtime's nearest read-only search/research equivalent; if no equivalent exists, record that gap in the Roster Decision.

Do not spawn a teammate whose agent type is not included in the recorded Roster Decision. general-purpose is a fallback, not a default: using it requires an explicit INCLUDE line explaining why no more specific specialist fits or why the runtime exposes no specialist type. If the task changes enough that a different specialist is needed, update the Roster Decision before spawning that teammate.

When deciding the agents to use, consider:

• 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.

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 the base branch from the ticket's target environment, then sync the working branch onto the latest of it before any work — so implementation always builds on current target-environment code:

1. Resolve the target environment from the resolved work item — its ## Target Backend Environment section (the field the *-write-* / *-add-journey skills record).
2. Map the environment to a base branch via .lisa.config.json deploy.branches (e.g. staging → staging, production → main) — the forward direction of the same map the env-keyed done resolution uses in reverse (see the config-resolution rule). If the work item names no environment, the base branch is the remote default branch (gh repo view --json defaultBranchRef -q .defaultBranchRef.name, or git remote set-head origin -a then read origin/HEAD). If the named environment is absent from deploy.branches, or its branch does not exist on the remote, stop and report — never guess a base.
3. Establish the feature branch off the latest base, conflict-free:
   • git fetch origin.
   • Already on a feature branch with an open PR → reuse it. If the PR's base ≠ the resolved base branch, surface the mismatch and re-target only with confirmation — the ticket's environment is the source of truth.
   • Already on a feature branch with no open PR → reuse it; its PR base will be the resolved base branch (do not ask the human — the environment determines it).
   • On an environment / default branch → check out a feature branch named for this plan (with the work-item ref prefix, per the linkage rules below) from origin/<base>.
   • Rebase the feature branch onto origin/<base> and resolve any merge conflicts BEFORE starting work. If the conflicts cannot be resolved cleanly and safely, create a fix task for the agent team (with the conflicting file list and current merge state) and resolve it before implementation begins — never start work on stale or conflicted code.
4. The PR targets the resolved base branch — carry it as target_branch=<base> into lisa:git-submit-pr (Verify flow). git-submit-pr already chooses a closing keyword when the base is the production/default branch and a non-closing reference for a non-terminal environment branch.

When the request came from a tracker work item, preserve its native identifier for development linkage:

• Capture tracker_provider and work_item_ref from the resolved input before creating or reusing a branch. Examples: github + CodySwannGT/lisa#614, linear + ENG-123, jira + ENG-123.
• If a new branch is needed and the provider can link branches by identifier, include the identifier in the branch name before the human-readable slug. Linear and JIRA integrations commonly link from branch names; GitHub issue linkage is PR-body driven, but including the issue number in the branch name is still useful. Keep branch names URL-safe, for example codex/ENG-123-add-checkout-copy or codex/614-add-checkout-copy.
• Pass the work-item ref and target branch to lisa:git-submit-pr when opening or updating the PR, for example work_item_ref=CodySwannGT/lisa#614 target_branch=<base resolved from the ticket's environment above> (not hardcoded main). The PR workflow owns provider-specific body text and must decide whether to use a closing keyword or a non-closing reference.
• After lisa:git-submit-pr returns a PR URL, ensure the reverse backlink is present on the source work item by running lisa:tracker-sync <work_item_ref> pr-ready pr_url=<url> tracker_provider=<provider>. The sync path must prefer native provider linkage and fall back to one managed [lisa-pr-link] comment when native linkage is unavailable or cannot be verified.
• If the provider has no native branch or PR development-linkage surface, continue without linkage and mention that the provider was skipped.

Using the general-purpose agent in Team Lead session, Determine which flow applies:

1. Research -- needs a PRD (no specification exists)
2. Plan -- needs decomposition (specification exists but no work items)
3. Implement -- has a well-defined work item
4. Verify -- has code ready to ship

If Implement, determine the work type:

1. Build (feature, story, task)
2. Fix (bug -- mandatory Reproduce sub-flow before investigation)
3. Improve (refactoring, optimization, coverage improvement)
4. Investigate Only (spike -- no code changes, just findings)

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:

1. Write a failing test that demonstrates the bug (preferred)
2. If a failing test is not possible, write a minimal reproduction script
3. Verify the reproduction is reliable (consistent failure)
4. The reproduction MUST succeed before any investigation or fix attempt begins
5. Examples of reproduction methods:
   1. Write a simple API client and call the offending API
   2. Start the server on localhost and use the Playwright CLI or Chrome DevTools

For any Fix flow, and for any Build flow that changes user-visible behavior, regression coverage is a required deliverable at the highest practical observation level for the reported surface. If the project has a browser, device, or end-to-end harness for that platform (for example Playwright, Maestro, Detox, Cypress, or an equivalent runtime), the task plan and definition of done MUST include a deterministic regression spec against the reported surface, using mocked or seeded data where needed. This is alongside unit or integration coverage, not a substitute for it.

The team lead may not waive, defer, demote, or phrase this regression spec as "optional", "if cheap", "nice to have", or equivalent. The only permitted exits are:

1. The project genuinely has no end-to-end harness for the affected platform; record the checked locations and that absence in the task metadata, PR, and work-item evidence.
2. A genuine technical blocker prevents adding or executing the spec in this PR; before merge, create a linked build-ready follow-up ticket, reference it from the PR and source work item, and keep the current item blocked or explicitly non-terminal until that follow-up is accepted.

Completion evidence for the regression spec must prove execution, not mere existence. A green CI run is insufficient unless the PR evidence includes a CI log line, reporter output, or equivalent record naming the new spec and showing that it ran and passed. Guard explicitly against test.skip, suite-level environment gates, shard filters, and "0 tests" passes.

If the required regression spec is still in flight on an auto-merge-enabled PR, pause auto-merge or use an equivalent merge gate until the spec commit is pushed and its execution proof is available. The flow must not allow the PR to merge before this non-demotable deliverable is satisfied or formally blocked through the linked follow-up path above.

Using the general-purpose agent in Team Lead session, determine how you will know that the task is fully complete. Write this as an effective completion condition — one an independent verifier could confirm from observed output alone, not from your assertion that it works. A strong condition has:

• One measurable end state — a status code, an exit code, a row count, an observable UI state, an empty queue. Not "it looks right" or "the code is correct".
• A stated proof command that surfaces the evidence — exactly how the running system is exercised so the result is observable (e.g. curl … returns 200 with {…}, "the Playwright run reaches the dashboard", "SELECT … returns the new row"). Quality gates (test/typecheck/lint) do NOT count — they are prerequisites.
• Constraints that must hold — anything that must not change on the way there (e.g. "no other endpoint's response changes", "no migration is dropped").

This condition is the contract the Verify flow proves and records in the verification verdict (below); it is what the completion gate checks before the flow may stop.

1. Examples
   1. Direct deploy the changes to dev and then Write a simple API client and call the offending API
   2. Start the server on localhost and then Use the Playwright CLI or Chrome DevTools

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 and surface its result in the transcript (NOT test/typecheck/lint, those are quality gates). Phrase it so an independent verifier sees the evidence, e.g. `curl -s localhost:3000/health` not `check that health works`",
    "expected": "the single measurable end state that proves success — observable system behavior (status code, response body, row count, UI state), not a subjective judgement"
  }
}

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.

For Fix tasks and user-visible Build tasks, testing_requirements must include the highest-practical-observation regression requirement above, including the selected harness or the recorded absence/blocker path. The completion condition must include the proof command and the required CI execution evidence for the new spec.

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:

1. Run quality gates: lint, typecheck, tests — all must pass. These are prerequisites, NOT verification.

2. verification-specialist: verify locally by running the actual system and observing results (empirical proof that the change works). This is the real verification step. For UI-surface bugs, the proof must observe the UI surface with browser/device automation against the target environment whenever such a harness exists; unit-level or API-only proof cannot satisfy the empirical verification contract for a UI-surface defect. 2a. Record the verification verdict — the independent, machine-readable proof that gates completion. The verification-specialist writes ${CLAUDE_PROJECT_DIR:-.}/.lisa/verification-status.json with one entry per acceptance criterion, each carrying the proof command's observed evidence:

   {
     "plan": "<plan-name>",
     "status": "pass | fail | blocked | in_progress",
     "criteria": [
       { "task": "<task id or title>", "criterion": "<the completion condition>", "status": "pass | fail", "evidence": "<the proof command run and the observed result>" }
     ],
     "updated_at": "<ISO8601 UTC>"
   }
   

   Set status: "pass" only when every criterion is pass with real evidence (output from running the system, not a claim). The verdict must be judged by an agent that did NOT implement the change (the verification-specialist), never self-certified by the implementer. This is runtime scratch — it is gitignored and MUST NOT be committed (treat it like the secrets exclusion in the commit step).

   On Claude, the enforce-verification-gate.sh Stop hook reads this file and will not let the flow stop until it shows a terminal, all-pass verdict — carrying over the non-bypassable completion gate of the /goal primitive, but checked deterministically against real evidence rather than by a transcript-only evaluator model. If you must stop before completion (a readiness gate failed, a blocker was found, a dependency is unresolved), write the verdict with status: "blocked" and the reason: that records the outcome and releases the gate instead of leaving it to spin. Other harnesses fall back to this prose obligation.

3. Write the highest-practical-observation regression test encoding the verification. For user-visible bugs or user-visible Build changes with an available browser/device/e2e harness, this means a deterministic spec on the reported surface. Prove the new spec actually executed and passed in PR CI by recording a named spec log/reporter line or equivalent execution record; green CI without that named evidence does not satisfy this step.

4. Record Implement usage on the originating work artifact via lisa:usage-accounting so the work item (or other implementation-owned artifact) gains a direct implement usage entry in the canonical ## Lisa Usage section. If the parent / child graph is already known, prefer record_and_rollup so ancestor totals refresh in the same write; otherwise still write the direct entry, and if runtime usage is unavailable, use source: unavailable with nullable token/cost fields instead of skipping the row.

5. Commit ALL outstanding changes in logical batches on the branch (minus sensitive data/information) — not just changes made by the agent team. This includes pre-existing uncommitted changes that were on the branch before the plan started. Do NOT filter commits to only "task-related" files. If it shows up in git status, it gets committed (unless it contains secrets).

6. Push the changes - if any pre-push hook blocks you, create a task for the agent team to fix the error/problem whether it was pre-existing or not

7. Open a pull request with auto-merge on via lisa:git-submit-pr, targeting the base branch resolved from the ticket's environment (target_branch=<base>, per the branch step above), and including the work-item ref when one exists so the PR can be linked natively to the source issue. 7a. Confirm two-way linkage before treating PR submission as complete: the PR body/title/branch must reference the work item, and the work item must have either a verified native PR link or a single managed [lisa-pr-link] fallback comment from lisa:tracker-sync.

8. PR Watch Loop: Drive the PR to merge via the drive-pr-to-merge skill — the single source of truth for clearing every blocker (auto-merge with direct-merge fallback, BEHIND re-sync, conflict resolution, failing-check fixes, human + bot review-comment handling with thread resolution, stale CHANGES_REQUESTED dismissal, and post-merge ancestry verification). git-submit-pr already invokes it; if you reach this step with a PR already open, invoke drive-pr-to-merge directly with the PR number. For a large review backlog you may fan the code-fix work out to the agent team, but drive-pr-to-merge owns the loop and the terminal conditions — do not re-implement them.

9. Merge the PR, then refresh the ticket-side backlink with lisa:tracker-sync <work_item_ref> pr-merged pr_url=<url> merge_sha=<sha> tracker_provider=<provider> when a work item exists.

10. Monitor the deploy action that triggers automatically from the successful merge

11. If deploy fails, create a task for the agent team to fix the failure, open a new PR and then go back to step 7

12. Remote verification: verification-specialist verifies in target environment (same checks as local verification, but on remote), and refreshes the verdict (step 2a) to reflect the remote result.

13. ops-specialist: post-deploy health check, monitor for errors in first minutes

14. If remote verification fails, create a task for the agent team to find out why it failed, fix it and return to step 5. Bound this loop: after a small number of full fix→deploy→reverify cycles without reaching a passing remote verdict (treat ~3 as the ceiling unless the work item states otherwise), stop retrying — file a build-ready fix ticket, write the verdict with status: "blocked" and the diagnosis, and move the work item to blocked rather than looping indefinitely. The completion gate releases on a blocked verdict, so the flow ends with a recorded outcome instead of a silent spin or a self-declared success.