Release Health

---

name: release-health description: Use when analyzing the health of an OCP release cycle — evaluates Features, Initiatives, Epics, and Tasks from Jira to assess progress, identify risks, surface refinement gaps, and recommend actions to keep the team on track toward branch cut allowed-tools: Agent, AskUserQuestion, Write, Read, Glob, Bash, mcp__plugin_mcp-atlassian_mcp-atlassian__jira_get_sprints_from_board, mcp__plugin_mcp-atlassian_mcp-atlassian__jira_search user-invocable: true

Release Health Analysis

You are orchestrating a release health analysis for the OCPEDGE team. Data fetching runs inline using MCP tools and transform scripts. Analysis is delegated to a sub-agent.

Before proceeding: Read plugins/edge-scrum/references/Edge-Scrum-Laws.md to find which law files apply to release health orchestration. For this skill, load: laws/00-team-roster.md, laws/01-jira-projects.md, laws/05-jira-features.md, laws/06-jira-fields.md, laws/09-sprint-policies.md, laws/14-agent-conventions.md. The configuration below is derived from the Laws — when in doubt, defer to the law files.

Configuration

# Scrum Board (skill-specific; not in Laws)
board_id: "11479"
board_name: "OpenShift Edge Scrum"
sprint_prefix: ["OCPEDGE Sprint", "OpenShift Edge Sprint"]

# Custom Field IDs (Red Hat Jira instance-specific)
fields:
  story_points:  customfield_10028   # Numeric; Stories/Tasks/Spikes
  epic_link:     customfield_10014   # Story → Epic relationship
  parent_link:   customfield_10018   # Epic → Feature/Initiative relationship
  qa_contact:    customfield_10470   # User picker; QA owner
  flagged:       customfield_10021   # Array; non-empty = impediment
  docs_approver: customfield_10473   # User picker; Doc Contact
  sme:           customfield_10475   # User picker; Subject Matter Expert

Execution Model

1. Steps 0–1: Load laws/roster, gather release parameters (main context)
2. Phase 2: Fetch sprints + features inline using MCP tools → transform scripts (main context)
3. Phase 3: Fetch epics + spikes inline using MCP tools → transform scripts (main context)
4. Phase 4: Delegate full analysis to sub-agent (LLM-driven risk assessment)
5. Step 9: Assemble and write the final report (main context)

Rules:

• Data fetching uses MCP tools directly in the main context
• MCP responses are large and get persisted to files automatically — note those file paths
• Transform scripts (plugins/edge-scrum/bin/) convert raw MCP data to structured JSON
• Use check-page.py to extract pagination info from persisted files
• The analysis sub-agent only needs Read, Write, and jira_search — it consumes the structured JSON
• Never embed raw Jira response data in the main context

User Arguments

The user may provide arguments: $ARGUMENTS

• Version number (e.g., 4.19, 5.0) → release version
• Sprint range (e.g., 281-285) → first through last sprint
• Branch cut (e.g., bc:285 or branch-cut 285) → last sprint before feature freeze
• --refinement → refinement-only mode (see below)
• No arguments → ask for all inputs

Refinement Mode (--refinement)

When the --refinement flag is present, the skill runs a narrower analysis focused on whether Features are fully refined to the story level:

1. Scope: Only Features/Initiatives in Refinement status are analyzed. Features already in Backlog, In Progress, or later states are excluded — they are past the refinement gate.
2. Data collection: Phases 2–3 run identically (sprints, features, epics, spikes are all fetched).
3. Analysis: Phase 4 spawns the analysis sub-agent with the REFINEMENT_MODE parameter set to true. The sub-agent runs all standard checks but adds a REFINEMENT_BY_SME output section that groups gaps by SME with natural language summaries.
4. Report: Step 9 uses a condensed refinement report instead of the full release-health template. It outputs the SME-grouped refinement view plus a summary. Sections irrelevant to refinement (Sprint Forecast, Schedule Risk) are excluded.

Set REFINEMENT_MODE = true when parsing arguments if --refinement is present; otherwise REFINEMENT_MODE = false.

---

Workflow

Step 0: Load Edge Scrum Laws and Roster (main context)

Read both files and hold in working memory:

1. Load these law files from plugins/edge-scrum/references/laws/:

   • 00-team-roster.md — team capacity and .roster.json structure
   • 01-jira-projects.md — Jira projects and OCPBUGS components — authoritative project keys and component names
   • 05-jira-features.md — Feature/Initiative conventions and sizing
   • 06-jira-fields.md — custom field IDs
   • 09-sprint-policies.md — sprint capacity rules
   • 14-agent-conventions.md — agent orchestration conventions

2. plugins/edge-scrum/.roster.json — extract:

   • Team roster — username, display_name, and sp_target per member
   • Roster size — count of members (used for capacity calculations)
   • If the file does not exist, stop and instruct the user to copy .roster.json.example to .roster.json and populate it.

The Laws are authoritative. Where this skill and the Laws conflict, the Laws win.

---

Step 1: Gather Release Parameters (main context)

Parse arguments. Use AskUserQuestion for any missing values:

1. Release version — e.g., 4.19, 5.0
2. Sprint range — first sprint number through last
3. Branch cut sprint — which sprint is the last dev sprint

Compute and confirm:

• FIRST = first sprint number
• LAST = last sprint number (branch cut)
• TOTAL_SPRINTS = LAST − FIRST + 1
• REFINEMENT_SPRINT_NUM = FIRST (dedicated to spike creation, no delivery expected)
• TOTAL_DEV_SPRINTS = TOTAL_SPRINTS − 1
• TODAY = today's date (YYYY-MM-DD)

Create the work directory:

WORKDIR=/tmp/release-health-$(date +%Y%m%d) && mkdir -p $WORKDIR && echo $WORKDIR

Record WORKDIR — substitute it into all agent prompts.

---

Phase 2: Sprint + Feature Collection (inline)

Fetch sprints and features in parallel using MCP tools, then transform with scripts.

2a — Fetch Sprints

Call jira_get_sprints_from_board for board_id "11479" three times:

• state="active"
• state="closed" — paginate using page_token (see pagination protocol below); use limit=50
• state="future"

After all pages are fetched, note all persisted file paths and run:

python3 plugins/edge-scrum/bin/transform-sprints.py \
  --input <all_persisted_file_paths> \
  --output {WORKDIR}/sprints.json \
  --today {TODAY} \
  --first-sprint {FIRST} \
  --last-sprint {LAST} \
  --total-dev-sprints {TOTAL_DEV_SPRINTS}

2b — Fetch Features

Call jira_search with:

• JQL (standard mode): project = OCPSTRAT AND issuetype in (Feature, Initiative) AND labels in ("ocpedge-plan", "microshift") AND "Target Version" = "openshift-{VERSION}" AND (resolution is EMPTY OR resolution not in (Duplicate, Obsolete)) ORDER BY Rank ASC
• JQL (refinement mode): project = OCPSTRAT AND issuetype in (Feature, Initiative) AND labels in ("ocpedge-plan", "microshift") AND "Target Version" = "openshift-{VERSION}" AND status = "Refinement" AND (resolution is EMPTY OR resolution not in (Duplicate, Obsolete)) ORDER BY Rank ASC
• Fields: key, summary, status, issuetype, priority, assignee, fixVersions, labels, description, issuelinks, customfield_10795, customfield_10470, customfield_10473, customfield_10475
• limit: 50

Use the refinement-mode JQL when REFINEMENT_MODE = true.

Paginate using page_token. If zero results, use fallback JQL (set fallback_used):

project = OCPSTRAT AND issuetype in (Feature, Initiative) AND labels in ("ocpedge-plan", "microshift") AND "Target Version" = "openshift-{VERSION}" AND status not in (Done, Closed) ORDER BY Rank ASC

After all pages fetched, run:

python3 plugins/edge-scrum/bin/transform-features.py \
  --input <all_persisted_file_paths> \
  --output {WORKDIR}/features.json

Append --fallback-used if fallback JQL was used.

2c — Verify

Read and check:

• {WORKDIR}/sprints.json — if "error" key is present or sprint_map is empty, warn the user and stop
• {WORKDIR}/features.json — if feature_keys is empty:
  • Refinement mode: Report "All features have progressed past Refinement — nothing to check." and exit cleanly (skip Phases 3–4 and Step 9).
  • Standard mode: Warn the user about scope and stop

---

Phase 3: Epic + Spike Collection (inline)

3a — Fetch Epics

Read {WORKDIR}/features.json. Extract feature_keys_csv.

If feature_keys has more than 50 entries, split into batches of 50. For each batch, call jira_search:

• JQL: project in (OCPEDGE, USHIFT) AND "Parent Link" in ({feature_keys_batch_csv}) ORDER BY Rank ASC
• Fields: key, summary, status, assignee, labels, description, parent, customfield_10028, customfield_10018, customfield_10470, customfield_10473, customfield_10475
• limit: 50

Paginate using page_token. After all pages fetched, run:

python3 plugins/edge-scrum/bin/transform-epics.py \
  --input <all_persisted_file_paths> \
  --output {WORKDIR}/epics.json

3b — Fetch Spikes

Read {WORKDIR}/sprints.json. Extract refinement_sprint_id.

Call jira_search:

• JQL: project in (OCPEDGE, USHIFT) AND issuetype = Spike AND sprint = {refinement_sprint_id}
• Fields: key, summary, status, assignee, issuelinks
• limit: 50

Paginate using page_token. After all pages fetched, run:

python3 plugins/edge-scrum/bin/transform-spikes.py \
  --input <all_persisted_file_paths> \
  --features-file {WORKDIR}/features.json \
  --epics-file {WORKDIR}/epics.json \
  --sprints-file {WORKDIR}/sprints.json \
  --output {WORKDIR}/spikes.json

3c — Verify

Read {WORKDIR}/epics.json and verify: epic_keys is a non-empty array, feature_to_epics is an object, and epics is an array. If any check fails, warn the user with a descriptive error and stop.

---

Pagination Protocol

This Jira instance uses page_token pagination, NOT start_at. Follow this protocol for all paginated MCP calls:

1. Make the first call without page_token

2. The response may be persisted to a file. Note the file path.

3. Run check-page.py to get pagination info:

   python3 plugins/edge-scrum/bin/check-page.py <persisted_file_path>
   

   Output: {"issues_count": N, "has_more": bool, "next_page_token": "..."}

4. If has_more is true: make the next call with page_token set to the next_page_token value. Repeat from step 2.

5. If has_more is false: pagination is complete.

For small responses that fit in context (not persisted), write them to {WORKDIR}/raw_<type>_<page>.json using Write, then run check-page.py on that file.

For jira_get_sprints_from_board: closed sprints may require multiple pages. Active and future typically fit in one page each.

---

Phase 4: Full Analysis (sub-agent)

Read plugins/edge-scrum/skills/release-health-analysis/SKILL.md. Substitute {WORKDIR}, {VERSION}, {TODAY}, {REFINEMENT_SPRINT_NUM}, {REFINEMENT_MODE}, then spawn as a sub-agent:

• Agent 4 — prompt: analysis content with all placeholders substituted

This agent reads all four data files, detects misplaced spikes, fetches child issues, builds the hierarchy, assesses all risks, and writes {WORKDIR}/analysis.md.

---

Step 9: Generate Report (main context)

Standard mode (REFINEMENT_MODE = false)

1. Read the report template from plugins/edge-scrum/references/release-health-report-template.md
2. Read {WORKDIR}/analysis.md
3. Parse the ===ANALYSIS_META=== block for header values
4. Substitute all {placeholder} values in the template:
   • Header fields from release parameters and ANALYSIS_META
   • {executive_summary}: Write 3-5 sentences using top_risks, overall_health, feature counts, and sprint_recommendation from ANALYSIS_META. Cover: overall verdict, count on track vs. at risk, top 2-3 risks, one recommended priority this sprint.
   • {DASHBOARD}, {FEATURE_DETAIL}, {EPIC_DETAIL}, {RISK_REGISTER}, {REFINEMENT_BACKLOG}, {SPRINT_FORECAST}, {ACTIONS}: paste the corresponding ===SECTION:*=== block content from analysis.md, stripping the sentinel lines
5. Add Jira links: replace all bare issue keys (OCPSTRAT-, OCPEDGE-, USHIFT-, OCPBUGS-) with [KEY](https://redhat.atlassian.net/browse/KEY) markdown links, skipping keys already inside link syntax
6. Write to .reports/release_health_{VERSION}_{TODAY}.md
7. Clean up: test -n "{WORKDIR}" && [[ "{WORKDIR}" == /tmp/release-health-* ]] && rm -rf -- "{WORKDIR}"

Refinement mode (REFINEMENT_MODE = true)

1. Read {WORKDIR}/analysis.md
2. Parse the ===ANALYSIS_META=== block for header values
3. Extract the ===SECTION:REFINEMENT_BY_SME=== block from analysis.md. If the section is not found, halt with an error: "Analysis phase did not produce the REFINEMENT_BY_SME section — check sub-agent output."
4. Extract the ===SECTION:REFINEMENT_BACKLOG=== block from analysis.md
5. Build the refinement report using the template from plugins/edge-scrum/references/refinement-report-template.md
6. Substitute placeholders in the template:
   • {VERSION}, {TODAY}, {REFINEMENT_SPRINT_NUM} — from release parameters
   • {refinement_state} — derive from sprints.json: "Closed" if refinement_sprint_closed = true, otherwise "Open"
   • {total_features} — from ANALYSIS_META total_features
   • {refined_count} — from ANALYSIS_META refined_count
   • {needs_attention_count} — from ANALYSIS_META needs_attention_count
   • {REFINEMENT_BY_SME} — paste the extracted section content, stripping sentinel lines
   • {REFINEMENT_BACKLOG} — paste the extracted section content, stripping sentinel lines
7. Add Jira links (same rule as standard mode)
8. Write to .reports/refinement_{VERSION}_{TODAY}.md
9. Clean up: test -n "{WORKDIR}" && [[ "{WORKDIR}" == /tmp/release-health-* ]] && rm -rf -- "{WORKDIR}"

---

Edge Cases

• No Features found: Try fallback JQL (handled in Phase 2b); warn user to confirm scope; stop if still empty. In refinement mode, an empty result means all features have progressed past Refinement — handled in Phase 2c.
• Feature with no Epics: Flag as "Unplanned"; epic fetch returns empty list for that feature.
• Epic with no Stories: Flag as "Empty" in analysis.
• Version format varies (4.19 vs 4.19.0): Analysis agent tries both in fixVersion queries.
• SME / Docs field errors: Transform scripts handle gracefully with fallback values.
• Sprint resolution failure: transform-sprints.py sets "error" in JSON; main context stops before Phase 3.
• Multiple spikes per Feature: transform-spikes.py records all in spike_keys[]; analysis agent uses worst-case status.
• OCPBUGS without Epics: Grouped under "(Unlinked Bugs)" section by analysis agent.
• Analysis during refinement sprint: Analysis agent skips 7a entirely — no delivery progress expected.
• Spike linked to child Epic: Detected by analysis agent via all_ref_sprint_spikes cross-reference with epics.json.

---

Important Notes

• Read-only: This skill does not modify any Jira data.
• Transform scripts: plugins/edge-scrum/bin/ — reusable data transformation (no LLM needed)
• Analysis sub-agent: plugins/edge-scrum/skills/release-health-analysis/SKILL.md — LLM-driven risk assessment and report generation
• Work directory: {WORKDIR} persists across phases within a run. Rerunning on the same day overwrites prior files.
• Laws files: The analysis sub-agent reads specific files from plugins/edge-scrum/references/laws/ per the index at plugins/edge-scrum/references/Edge-Scrum-Laws.md — never hardcode roster, rules, or sizing in skill definitions.