plugins/src/base/skills/lisa-jira-journey/SKILL.md
Parse a JIRA ticket's Validation Journey section, execute the verification steps using appropriate tools (curl, test commands, database queries), capture evidence, and post to JIRA + GitHub PR using the jira-evidence skill.
npx skillsauth add codyswanngt/lisa lisa-jira-journeyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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All Atlassian operations in this skill go through lisa-atlassian-access. Do not call MCP tools or acli directly. Note: the helper scripts (scripts/parse-plan.py, scripts/jira-evidence/post-evidence.sh) currently use direct API calls and are pending migration to route through atlassian-access.
Parse a JIRA ticket's Validation Journey, execute the verification steps using the appropriate tools for the change type, capture evidence at each typed [EVIDENCE: <artifact-type>: <name>] marker, and post to JIRA + GitHub PR.
$ARGUMENTS: <TICKET_ID> [PR_NUMBER]
TICKET_ID (required): JIRA ticket key (e.g., PROJ-123)PR_NUMBER (optional): GitHub PR number to update descriptionJIRA_API_TOKEN environment variable setjira-cli configured (~/.config/.jira/.config.yml)gh CLI authenticatedRun the parser script to extract the Validation Journey from the JIRA ticket description:
python3 .claude/skills/jira-journey/scripts/parse-plan.py <TICKET_ID>
The script outputs JSON with: ticket, prerequisites, steps, viewports, assertions.
Note: viewports may be empty for TypeScript tickets — that is expected.
Before starting the journey, verify each prerequisite:
Execute each step sequentially. For each step, determine the verification approach based on the step text and change type:
evidence/NN-name.txtevidence/NN-name.txtevidence/NN-name.txtevidence/NN-name.txtevidence/NN-name.txtAt each typed [EVIDENCE: <artifact-type>: <name>] marker, capture an artifact of the declared type — the type is the contract, not a suggestion:
screenshot / recording → an actual image/video file from the driven UI (Playwright, simulator), never a text description of what was seenhttp-transcript → the exact request (curl command or client call) plus the full responsecli-output → the command plus stdout/stderr and exit codelog-snippet → the correlated log lines pulled from the running systemdb-query-output → the query plus returned rowsperf-trace → the benchmark/frame-timing/profiler output with methodology (device profile, dataset size)test-run-log → reporter output naming the spec and showing it ran and passeddeploy-log / state-dump → the deployment/health-check output or observed-state JSONA prose claim ("the error state rendered gracefully") satisfies no marker. Legacy untyped markers: infer the type from the step's action, capture accordingly, and note the inference. Write each artifact to a numbered file:
Treat only exact [EVIDENCE: ...] markers (plus the legacy local [SCREENSHOT: ...] form) as capture instructions. Both the canonical [EVIDENCE-REF: <work-item-ref> | <artifact-type>: <kebab-case-name>] and the Lisa 2.223.0 legacy alias [EVIDENCE-REF: <tracker-ref>: <artifact-type>: <kebab-case-name>] point to another work item's artifact: preserve either as explanatory text, but do not capture it, assign it a sequence number, include it in duplicate-name checks, or count it as a local manifest entry. If a runtime-changing leaf has references but no local claiming marker, stop because S14 is unsatisfied.
Evidence files are named: {NN}-{evidence-name}.{ext} — extension matches the declared artifact type (.png/.webm for screenshot/recording, .txt for transcripts/logs/output, .json for structured state)
NN: zero-padded sequential number (01, 02, 03...)evidence-name: the <name> part of the typed marker in the JIRA stepExample:
evidence/
01-health-check.json
02-schema-after-migration.txt
03-rate-limit-response.txt
comment.txt
comment.md
After capturing all evidence, run the template generator:
python3 .claude/skills/jira-journey/scripts/generate-templates.py \
<TICKET_ID> \
<PR_NUMBER> \
<BRANCH_NAME> \
./evidence
This generates evidence/comment.txt (JIRA wiki markup) and evidence/comment.md (GitHub markdown) with evidence formatted as code blocks.
Use the jira-evidence skill to post everything:
bash .claude/skills/jira-evidence/scripts/post-evidence.sh <TICKET_ID> ./evidence <PR_NUMBER>
Confirm evidence is posted to both the JIRA ticket and GitHub PR.
The agent should use patterns from the project's verfication.md when executing steps:
| Change Type | Verification Method | Evidence Format |
|---|---|---|
| API endpoint | curl -s localhost:PORT/endpoint | JSON response |
| Database migration | psql -c "\d table" or migration output | Schema text |
| Background job | Trigger + check state | Log output |
| Library/utility | bun run test -- path/to/test | Test output |
| Security fix | Reproduce + verify fix | Request/response |
| Auth/authz | Multi-role verification | Status codes per role |
Ensure the command succeeded and produced output. Use 2>&1 to capture both stdout and stderr.
The ticket may not have a Validation Journey section. Use /jira-add-journey <TICKET_ID> to add one.
development
Prepare a machine — a fresh laptop or a throwaway container — to run coding agents, before any repository exists. Detects which of Lisa's supported agents (Claude Code, Codex, Cursor, OpenCode, Antigravity, Copilot) are already installed, asks which credential manager the machine uses (Bitwarden, 1Password, Doppler, Vault, AWS, or none), and installs only what is missing, each by its vendor's own preferred method. Idempotent, headless by default, and emits a Dockerfile for a spin-up/spin-down environment. Run it on a new machine, in a container, or before cloning anything.
tools
Provision and verify a remote execution environment for a host project — Codex Cloud today, other remote surfaces as they are added. Generates a repository-owned setup script that installs the declared toolchain, materializes secrets through lisa-secrets-access, and runs the project's own hook. Provisions by API where one exists, by driving the vendor console where one does not, and by emitting exact config otherwise — then proves the result with the same read-back regardless of which tier did the work. Use before dispatching any work with executionEnv.
tools
Bring a developer's machine in line with the toolchain the project declares. Reports every tool in remoteEnv.tools that is missing, outdated, or unpinned for this platform, and installs the missing ones into ~/.local/bin from the same pinned, checksummed entries the remote surfaces use — but only when asked. Same manifest, same pins, same installers as lisa-setup-remote-env; what differs is consent and that the pin is a floor rather than an equality. Run it on a fresh checkout, after a manifest change, or when a tool fails at the moment of use.
tools
Route one unit of work to a remote execution surface. Reads the executionEnv parameter (local by default, codex-cloud or claude-web today), verifies the environment is provisioned and bound to this repository, submits a thin skill invocation, records the task identifier to .lisa/remote-dispatch.json, and exits without polling. Routing only — the remote runs the identical skill from the identical repository. Composable and inline: other skills invoke it via the Skill tool rather than users calling it directly.