docs-ticket-readiness
Skill4 starsChanged 4 months agoApache-2.0View on GitHub
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.
In this repository53 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- action-commentsSkill
- article-extractorSkill
- docs-convert-gdoc-mdSkill
- docs-orchestratorSkill
- docs-review-content-qualitySkill
- docs-review-modular-docsSkill
- docs-review-securitySkill
- docs-review-styleSkill
- docs-review-technicalSkill
- docs-workflow-code-analysisSkill
- docs-workflow-create-jiraSkill
- docs-workflow-create-merge-requestSkill
- docs-workflow-jira-readySkill
- docs-workflow-pipeline-diagnosticsSkill
- docs-workflow-planningSkill
- docs-workflow-pr-analysisSkill
- docs-workflow-quality-gateSkill
- docs-workflow-requirementsSkill
- docs-workflow-scope-req-auditSkill
- docs-workflow-security-reviewSkill
- docs-workflow-startSkill
- docs-workflow-style-reviewSkill
- docs-workflow-tech-reviewSkill
- docs-workflow-writingSkill
- git-pr-readerSkill
- ibm-sg-audience-and-mediumSkill
- ibm-sg-language-and-grammarSkill
- ibm-sg-legal-informationSkill
- ibm-sg-numbers-and-measurementSkill
- ibm-sg-punctuationSkill
- ibm-sg-referencesSkill
- ibm-sg-structure-and-formatSkill
- ibm-sg-technical-elementsSkill
- jira-readerSkill
- jira-writerSkill
- learn-codeSkill
- lint-with-valeSkill
- mdita-writeSkill
- query-codeSkill
- redhat-docs-tocSkill
- rh-ssg-accessibilitySkill
- rh-ssg-formattingSkill
- rh-ssg-grammar-and-languageSkill
- rh-ssg-gui-and-linksSkill
- rh-ssg-legal-and-supportSkill
- rh-ssg-release-notesSkill
- rh-ssg-structureSkill
- rh-ssg-technical-examplesSkill
- rn-known-issuesSkill
- understand-pull-requestSkill
- validate-plugin-prereqsSkill
The file
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 3 are distinctive, and 9 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
9
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 24
If first arg looks like a JIRA key (matches ^[A-Z][A-Z0-9]+-\d+$): single ticket mode with --issue <key>.
- Very specific: line 25
If --jql is present: batch mode.
- Distinctive: line 26
If neither: ask the user for a ticket key.
- Distinctive: line 30
Run the readiness script to fetch JIRA data and perform Dimensions 2–4 (PR linkage, metadata, relationships):
- Very specific: line 53
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 …
- Very specific: line 84
If a subagent fails (e.g., PR is private or token missing), set that PR's verdict to {"url": "...", "relevant": null, "reason": "Could not …
- Very specific: line 88
If --skip-description-check was passed, set description_quality to {"status": "skipped"} for all tickets and skip to Step 3.
- Very specific: line 93
If description_text is empty or very short (under 20 characters), set:
- Very specific: line 132
If --comment was passed:
- Distinctive: line 138
If the user declines, skip comment posting.
- Very specific: line 150
If --output-dir was passed, run the script with the output-dir flag.
- Very specific: line 195
Remove description_text from the JSON before displaying — it is internal data used for agent assessment, not for the user.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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-commentReport 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/60Batch — 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 readyRemove description_text from the JSON before displaying — it is internal data used for agent assessment, not for the user.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

