Docs ticket readiness
Skill opendatahub-io/docs-skills/skills/docs-ticket-readiness
Assess JIRA ticket readiness for the docs-orchestrator workflow. Checks description quality (via LLM), PR/source linkage, metadata completeness, and relationship context (2-level graph traversal). Outputs structured JSON verdict with actionable gaps. Use when you want to check if a JIRA ticket has enough information before starting a docs workflow.From its SKILL.md
npx -y skills add opendatahub-io/docs-skills --skill docs-ticket-readinessAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
8.3 KB, as published. Nobody here has run it
Ticket Readiness Assessment
Standalone skill that evaluates whether a JIRA ticket has sufficient information for the docs-orchestrator workflow to succeed. Checks four dimensions and produces a structured verdict.
Parse arguments
$1— JIRA ticket key (e.g., PROJ-123). Mutually exclusive with--jql.--jql <query>— JQL query for batch mode. Mutually exclusive with positional ticket key.--output-dir <path>— Write per-ticket markdown reports to this directory.--max-results <n>— Max tickets for JQL mode (default: 10).--skip-description-check— Skip LLM description quality assessment (mechanical checks only).--comment— Post readiness verdict as a JIRA comment after assessment.--ready-statuses <list>— Comma-separated JIRA statuses considered docs-ready (default: Done, Closed, Resolved, In Review, Code Review, Release Pending).
Determine mode:
- If first arg looks like a JIRA key (matches
^[A-Z][A-Z0-9]+-\d+$): single ticket mode with--issue <key>. - If
--jqlis present: batch mode. - If neither: ask the user for a ticket key.
Step 1: Run mechanical checks
Run the readiness script to fetch JIRA data and perform Dimensions 2–4 (PR linkage, metadata, relationships):
uv run --script ${CLAUDE_SKILL_DIR}/scripts/ticket_readiness.py \
--issue <TICKET_KEY> \
--plugin-root ${CLAUDE_PLUGIN_ROOT} \
[--ready-statuses <list>]
For batch mode:
uv run --script ${CLAUDE_SKILL_DIR}/scripts/ticket_readiness.py \
--jql "<JQL_QUERY>" \
--max-results <N> \
--plugin-root ${CLAUDE_PLUGIN_ROOT} \
[--ready-statuses <list>]
Capture the JSON output. If the output contains an "error" key at the top level, report the error to the user and STOP.
Step 1.5: PR relevance assessment
If PRs were discovered in Step 1 (i.e., dimensions.pr_source_linkage.checks.git_links_present.status is pass or warn), assess whether each PR is actually relevant to the JIRA ticket.
-
Collect all PR URLs from the Step 1 JSON. Look in:
dimensions.pr_source_linkage.checks.git_links_present.detail(parse PR URLs)relationship_map.children[].prentriesrelationship_map.documented_by[].prentries
-
For each unique PR URL, dispatch a subagent (using the Agent tool) in parallel (all Agent calls in one message). Each subagent should:
a. Run the git PR reader to fetch PR info:
uv run --script ${CLAUDE_PLUGIN_ROOT}/skills/git-pr-reader/scripts/git_pr_reader.py info <PR_URL>b. Compare the PR title and description against the JIRA ticket summary and description (from
description_textin the Step 1 JSON).c. Return a single-line JSON verdict:
{"url": "<PR_URL>", "relevant": true, "reason": "PR adds OpenClaw deployment manifests matching the ticket's scope"}or:
{"url": "<PR_URL>", "relevant": false, "reason": "PR is a CI pipeline fix unrelated to the feature"} -
Collect all subagent verdicts and merge into the output:
- Add
dimensions.pr_source_linkage.checks.pr_relevancewith:status:passif all PRs relevant,warnif any irrelevant,infoif no PRs to checkdetail: summary (e.g., "2/2 PRs relevant" or "1/3 PRs irrelevant: github.com/org/repo/pull/99")verdicts: array of the per-PR verdict objects
- Add
-
If a subagent fails (e.g., PR is private or token missing), set that PR's verdict to
{"url": "...", "relevant": null, "reason": "Could not fetch PR info"}and do not count it toward pass/warn.
Step 2: Description quality assessment (Dimension 1)
If --skip-description-check was passed, set description_quality to {"status": "skipped"} for all tickets and skip to Step 3.
Otherwise, for each ticket in the output where dimensions.description_quality is null:
- Extract
description_textfrom the JSON. - If
description_textis empty or very short (under 20 characters), set:{"status": "fail", "score": 1, "gaps": ["Description is empty or a one-liner"]} - Otherwise, assess the description against this rubric:
Rubric for description quality assessment:
| Signal | Weight | Pass | Warn | Fail |
|---|---|---|---|---|
| Specificity | High | Describes a concrete change/feature with technical detail | Vague but has some context | One-liner or empty |
| User impact | Medium | Explains what users need to know or do differently | Implied but not stated | No user-facing context |
| Acceptance criteria | Medium | Has explicit ACs or clear definition of done | Some criteria but informal | None present |
| Scope clarity | Low | Clear boundaries of what's in and out of scope | Partially bounded | Unbounded or ambiguous |
Produce a verdict as JSON with this exact structure:
{"status": "pass|warn|fail", "score": 1-5, "gaps": ["gap description 1", "gap description 2"]}
Rules:
- Score 4-5 → status "pass"
- Score 3 → status "warn"
- Score 1-2 → status "fail"
gapsshould list specific, actionable gaps (e.g., "No acceptance criteria specified", "User impact not described"). Empty array if status is "pass".
- Merge the verdict into the ticket's JSON under
dimensions.description_quality.
Step 3: Compute overall verdict
For each ticket, compute overall_status from all four dimensions:
ready— no fails in any dimensionready_with_warnings— no fails, but at least one warningnot_ready— at least one fail in any dimension
Dimensions with status null, skipped, or info do not count toward the verdict.
Step 4: Post JIRA comment (if --comment)
If --comment was passed:
For batch mode (multiple tickets): First confirm with the user:
"This will post readiness comments to N JIRA tickets. Continue?"
If the user declines, skip comment posting.
Post comments: Pipe the final merged JSON (with description_quality filled in) into the script's --post-comment mode:
echo '<MERGED_JSON>' | uv run --script ${CLAUDE_SKILL_DIR}/scripts/ticket_readiness.py --post-comment
Report the comment posting results to the user.
Step 5: Write markdown reports (if --output-dir)
If --output-dir was passed, run the script with the output-dir flag. The script writes per-ticket markdown reports to <output-dir>/<TICKET>-readiness.md.
Alternatively, if the script was already run with --output-dir in Step 1, the reports are already written. Confirm the output path to the user.
Step 6: Present results
Single ticket — formatted summary:
## PROJ-123: Add widget API documentation
**Verdict: READY** (or READY WITH WARNINGS / NOT READY)
| Dimension | Status | Details |
|-----------|--------|---------|
| Description quality | PASS (4/5) | — |
| PR/source linkage | PASS | 2 PRs found (2/2 relevant) |
| Metadata | WARN | Release note type not set |
| Relationships | PASS | Parent: PROJ-100 (Epic) |
### Relationship Map
- Parent: PROJ-100 (Epic: Widget Platform)
- PROJ-123 (Story: Add widget API documentation) ← this ticket
- PROJ-458 (Story: Widget docs) — sibling
- Children:
- PROJ-456 (Sub-task: Widget API endpoints) — PR: github.com/org/repo/pull/55
- PROJ-457 (Sub-task: Widget UI components)
- Documented by:
- PROJ-789 (Task: Update API reference) — PR: github.com/org/repo/pull/60
Batch — summary table:
## Readiness Assessment: N tickets
| Ticket | Summary | Verdict | Gaps |
|--------|---------|---------|------|
| PROJ-123 | Add widget API | READY | — |
| PROJ-456 | Fix auth bug | NOT READY | No PRs, missing fix version |
| PROJ-789 | Update config | READY (warnings) | Release note type not set |
**Summary:** 1 ready, 1 ready with warnings, 1 not ready
Remove description_text from the JSON before displaying — it is internal data used for agent assessment, not for the user.