create-epic-recap

name	create-epic-recap
description	Use when an engineer or manager asks to recap, summarize, or post an update on a Jira Epic — a progress update for an in-progress Epic (how far along it is, what's shipped so far, what's next) or a resolution recap for a finished one. Gathers child-issue progress, merged GitHub PRs, release notes, and Epic/child comments, previews a stakeholder-ready recap, and posts only after approval.
argument-hint	<EPIC-KEY e.g. OTAGENT-820> [--dry-run]
model	sonnet
allowed-tools	Bash, Read, Write, Glob, Grep, AskUserQuestion, mcp__atlassian__getJiraIssue, mcp__atlassian__searchJiraIssuesUsingJql, mcp__atlassian__getJiraIssueRemoteIssueLinks, mcp__atlassian__addCommentToJiraIssue, mcp__atlassian__getAccessibleAtlassianResources

Generate a recap for the Jira Epic $ARGUMENTS, aggregating child-issue progress, merged GitHub PRs, and release notes. The recap adapts to the Epic's state: a progress update while it's in flight (how far along it is, what's shipped so far, what's next) or a resolution recap once it's done — see Determine the recap mode in Step 2. Show a preview and post it as a comment on the Epic only after explicit user approval. This lets an engineer communicate progress or resolution to PMs and stakeholders without losing flow (motivation: OTAGENT-1038).

Owning team: team/opentelemetry-agent (@DataDog/opentelemetry-agent)

Reference files (load as needed)

• references/runtime-tooling.md — runtime detection, the Cursor vs Claude Code tool mapping, cloudId, large responses, JQL. Read this before Step 2.
• references/pr-discovery.md — the full Step 4 algorithm: Phase A/B, tier classification + regex, drop rules, throttling, the {{pr_discovery_note}} variants, and the Claude Code capability gap. Read this before Step 4.

This skill runs in two runtimes with different Atlassian MCP servers (Cursor's mcp-atlassian and Claude Code's Atlassian Rovo). The key gap: Rovo has no dev-status endpoint, so Phase A1/Tier 0 is Cursor-only and cloudId is required on every Rovo call. Whenever a step says "call the Fetch issue / Search children / Post comment tool", look up the exact tool and params in references/runtime-tooling.md.

Prerequisites

If any check fails, stop and tell the user what to fix.

1. Atlassian MCP server — connected and authenticated (Cursor: user-atlassian; Claude Code: Atlassian Rovo). Probe with a known issue fetch; if it fails, ask the user to authenticate/connect.
2. GitHub CLI (gh) — installed and authenticated for the DataDog org. Run gh auth status; if no active account, ask the user to run gh auth login.

Example

Input: /create-epic-recap OTAGENT-304 --dry-run

Fetches Epic OTAGENT-304, finds its completed child issues, discovers merged PRs across all child keys (Jira Development panel on Cursor + GitHub search), reads release notes from the PR file lists, renders the recap, prints a preview, and — because of --dry-run — saves a draft without posting:

Saved draft to /tmp/OTAGENT-304-recap.md

Step 1: Parse arguments

• EPIC-KEY (required, first positional): matches ^[A-Z][A-Z0-9_]+-\d+$, e.g. OTAGENT-820. If missing or malformed, stop and ask the user.
• --dry-run (optional flag): render and preview only, never post.
• --mode <resolution|progress> (optional): override the recap mode. When omitted, Step 2 auto-detects it from the Epic status. Use progress for an in-progress Epic (a status update on how far along it is) and resolution for a finished Epic.

Step 2: Fetch the Epic

Call the Fetch issue tool (see references/runtime-tooling.md) requesting fields summary, description, status, issuetype, labels, assignee, reporter and the Epic's comments (Cursor: comment_limit: 20; Claude Code: add "comment" to the fields array with responseContentFormat: "markdown"). See Reading comments in references/runtime-tooling.md.

Validate:

• If the issue cannot be found, stop and inform the user.
• Read the issue type from whichever shape the runtime returns — accept both issuetype.name (Rovo) and issue_type.name (some mcp-atlassian versions). If the resolved name is not Epic, stop and tell the user this skill only works on Epics (suggest /run-jira for non-Epics). Do not reject just because one of the two shapes is absent.

Determine the recap mode (epic_mode), used from here on to shape wording and sections:

• If --mode was passed in Step 1, use it verbatim.
• Otherwise auto-detect from the Epic's status.category (accept status.statusCategory.key too): category Done → resolution; anything else (indeterminate/In Progress, new/To Do) → progress.
• resolution = the Epic is finished, produce a "Resolution recap". progress = the Epic is still in flight, produce a "Progress update" (how far along it is, what's shipped so far, what's next).

Read the Epic comments you fetched: skim the most recent ones for context that is not in the description or PRs — decisions, scope changes, blockers, and (especially in progress mode) status updates on how far along the work is. Capture this as epic_comment_context for {{summary}} and the progress narrative.

Keep summary, description, status, labels, epic_mode, and epic_comment_context in memory for rendering.

Step 3: Fetch child issues

Call the Search children tool with the Epic-children JQL (see references/runtime-tooling.md), fields: summary, status, issuetype, assignee, labels, limit 50. On Claude Code, also request customfield_10000 in this call so Step 4 Phase A2 counts come back for free. If the response spills to a file, parse with jq.

Collect each child's key, summary, status.name, and status.category (accept status.statusCategory.key too).

Classify children by status category into three buckets:

• done_children — category Done (statuses like Done, Closed, Resolved).
• in_progress_children — category indeterminate (In Progress, In Review, etc.).
• todo_children — category new (To Do, Backlog, etc.).

Compute progress counts for rendering: done = len(done_children), total = <count of all children>, percent = round(100 * done / total) (guard against total == 0).

• In resolution mode, PR discovery and the recap body are driven by done_children; unfinished items go into skipped_children and are only mentioned if asked (as before).
• In progress mode, done_children still drive PR discovery (merged PRs), while in_progress_children and todo_children are surfaced in the Progress section as remaining work.

Read comments on the relevant child issues too — useful context often lives only in task comments, so don't skip them. For each relevant child (in resolution mode: done_children; in progress mode: prioritise in_progress_children, then done_children), call the Fetch issue tool individually with comment_limit / fields:["comment"] and skim the latest comments. Do not request the comment field in the bulk Search children call (it blows up the response — see Large responses / Reading comments in references/runtime-tooling.md). Bound the work: cap at ~10 issues and the latest ~10 comments each; capture anything material as child_comment_context.

An empty list of completed children is fine — some Epics are resolved by PRs that reference the Epic key directly. Continue with just <EPIC-KEY> as the search term.

Step 4: Find merged PRs

Read references/pr-discovery.md and follow it. In short:

• Build the key list [EPIC-KEY, <completed child keys>]. PR discovery is merged-only: even in progress mode only done_children contribute keys — in_progress_children/todo_children are represented as remaining work in the Progress section, not searched for PRs.
• Phase A (Cursor only): A1 reads Tier 0 PRs from the Jira Development panel; A2 reads merged-PR counts from customfield_10000 into jira_pr_counts for cross-validation. On Claude Code, skip A1 (no dev-status) and use A2 + Phase B only.
• Phase B (both runtimes): gh search prs once per key, then classify each hit into Tier 1 (include) / Tier 2 (include) / Tier 3 (opt-in, surfaced in preview) / Tier 4 (cross-ref, drop).
• Apply the revert/bot drop rules, dedup across phases, and record tier3_candidates and pr_shortfall.
• If zero PRs are found, use the manual-URL / empty-section / cancel fallback from the reference.

Step 5: Fetch PR details

For each merged PR, fetch details (run in parallel when possible):

gh pr view <number> \
  --repo <owner>/<repo> \
  --json title,body,files,labels,mergedAt,baseRefName,author,mergeCommit

Collect:

• title, body, mergedAt, baseRefName.
• mergeCommit.oid — store as mergeSha; Step 6 needs it to read release-note files added by the PR that aren't on the base branch. If mergeCommit is null (rebase/squash merge), fall back to the last commit's oid: gh pr view <number> --repo <owner>/<repo> --json commits --jq '.commits[-1].oid'.
• files[].path — used in Steps 6 and 7.
• labels[].name — note team/opentelemetry, component/*, changelog/*, qa/*.

Step 6: Read release notes from the PRs

For each PR, filter files[].path for entries starting with releasenotes/notes/ (main Agent), releasenotes-dca/notes/ (Cluster Agent), or releasenotes-installscript/notes/ (Install script). PRs may live in datadog-agent or other Datadog repos using the same convention.

Fetch each matching path from the PR's baseRefName:

gh api "repos/<owner>/<repo>/contents/<path>?ref=<baseRefName>" --jq '.content' | base64 -d

If the file was added by the PR (not yet on base) or has since been removed, fall back to the merge commit via mergeSha:

gh api "repos/<owner>/<repo>/contents/<path>?ref=<mergeSha>" --jq '.content' | base64 -d

If mergeSha is unavailable, skip the file and note its release note could not be read — do not fail; Step 9's PR-body fallback covers it.

Parse each YAML note and collect the section name (features, enhancements, fixes, upgrade, deprecations, security, other, issues) and its prose. Keep the original wording — release notes are already customer-facing.

Empty release notes are common, not an error. Several teams (notably team/opentelemetry-agent, which routinely labels DDOT PRs changelog/no-changelog) ship user-visible behaviour without reno entries. If none are found, do not stop or warn — Step 9 derives What's new from PR titles/bodies. Record this so the preview can note _None of the linked PRs included release notes_.

Step 7: Classify the change

Build a signals object from PR file paths and release-note prose. Each field can have multiple values; omit it from the recap when no signal matches.

Signal path (file-path prefixes):

• comp/otelcol/, comp/core/configsync/, cmd/otel-agent/, pkg/config/otel/ → agent-otel-ingest and/or ddot
• pkg/opentelemetry-mapping-go/ → dd-exporter-contrib
• Helm charts, chart/, Dockerfile.otel, images/otel-agent/ → standalone-ddot

Signal type (file-path prefixes; a change can hit several):

• pkg/logs/, comp/logs/ → logs
• pkg/metrics/, pkg/opentelemetry-mapping-go/otlp/metrics/, comp/metrics/ → metrics
• pkg/trace/, cmd/trace-agent/ → traces
• pkg/collector/corechecks/ebpf/, pkg/gpu/, pkg/security/, pkg/profiler/ → profiles/system

API & config changes — scan PR diffs and release-note content for paths like pkg/config/setup/config.go, pkg/config/**/*.yaml, comp/core/config/, cmd/*/subcommands/*/command.go, or prose with config/option/setting/API/endpoint/flag. If found, list the concrete config keys / API surfaces (from release notes when available, else the diff). Otherwise mark "None".

Repositories touched — distinct repository.nameWithOwner from Step 4, sorted alphabetically.

Step 8: Ask the user for the remaining sections

Use a single multi-question AskUserQuestion for the pieces that cannot be derived from code, each with a free-text option plus the canned answer:

1. Performance impact — text; Not measured valid. Encourage benchmark numbers / load-test / regression-detector links.
2. Agent footprint — text; No change valid. Encourage RSS / CPU / binary-size deltas with quality-gates dashboard links.
3. Customer utilisation tracking — how PMs track adoption: dashboard URL, metric name, log query, telemetry event, or Not tracked yet.

Step 9: Render the recap

Read recap-template.md and substitute each {{placeholder}}:

Placeholder	Source
{{epic_key}}	Step 1
{{epic_summary}}	Step 2
{{recap_title}}	Step 2 epic_mode: Resolution recap (resolution) or Progress update (progress).
{{summary}}	Synthesised 1-2 sentences for PMs, informed by epic_comment_context. resolution: what shipped and that the Epic is done — prefer Epic summary + release-note headlines; if no release notes, combine the Epic summary with the most user-relevant PR titles. progress: where the work stands — what's shipped so far and what's next, leading with the progress count.
{{progress}}	progress mode only (omit the section otherwise). From Step 3: a bold **<done> of <total> issues complete (<percent>%).** line, then a Remaining: bullet list of in_progress_children (label In progress) and todo_children (label To do) as [<KEY>](<url>) — <summary>. Fold in status notes from epic_comment_context / child_comment_context when they explain where things stand.
{{whats_new}}	Bullet list of user-facing wins, in order of preference: (1) features/enhancements release-note prose; (2) fixes/upgrade/deprecations if user-visible; (3) fallback when release notes are empty: one bullet per PR from the title (strip the [OTAGENT-XXX] prefix, rewrite in user-facing language) + a one-sentence summary of the PR body's ### What does this PR do?. The fallback is the normal path for changelog/no-changelog teams. Drop internal refactors, behaviourless dep bumps, and test-only PRs.
{{signal_path}}	Step 7 bullet list, or omit the section if empty
{{signal_type}}	Step 7 bullet list, or omit the section if empty
{{api_config_changes}}	Step 7 content, or omit if "None" and no relevant release notes
{{performance_impact}}	Step 8 answer, or omit if Not measured AND no perf-related release notes
{{agent_footprint}}	Step 8 answer, or omit if No change AND no footprint-relevant release notes
{{repositories_touched}}	Step 7 list, bullet form
{{customer_tracking}}	Step 8 answer, or omit if Not tracked yet
{{linked_prs}}	Bullet list - [<repo>#<number>](<url>) — <title>, then indented release-note bullets ( - <section>: <one-line excerpt>). Group Tier 0 PRs first with a _(linked via Jira)_ annotation, then Tier 1/2 from GitHub search. On Claude Code there are no Tier 0 PRs — start with Tier 1/2.
{{pr_discovery_note}}	One of the quiet/loud/empty variants — see PR discovery note in references/pr-discovery.md. Quiet whenever Tier 0 was unavailable (always on Rovo) and no shortfall; loud whenever pr_shortfall is non-empty; empty only when Tier 0 was available (Cursor) and pr_shortfall is empty.

Drop the HTML rendering-rules comment from the template before producing the final markdown. When omitting an optional section, remove its ## heading too — no empty headings.

Step 10: Preview and approval

Print the rendered markdown under ### Preview — <EPIC-KEY> recap.

Before the recap, print a one-line PR discovery summary (on Claude Code the Tier 0 count is always 0 — make clear discovery was GitHub-only):

> Found N PRs: X via Jira Development panel (Tier 0), Y via GitHub search (Tier 1/2). Z Tier 3 candidates skipped (see below).

If pr_shortfall is non-empty, print a warning block after the summary (the same shortfall is also rendered into the posted report via {{pr_discovery_note}}):

> ⚠️ OTAGENT-307: Jira says 4 linked PRs, found 2. Check Tier 3 candidates or the Jira Development panel.

After the recap, if tier3_candidates is non-empty, print a separate ### Skipped (Tier 3 — opt-in) block (shown to the user only, not posted to Jira):

### Skipped (Tier 3 — opt-in)

The following PRs mention the searched Jira keys in their body but without a closing keyword (`Resolves`/`Closes`/`Fixes`/`JIRA:`). They are excluded by default. Pick `Edit` and say "include #N, #M" to add them.

- [<repo>#<number>](<url>) — <title>
  - Searched key: <KEY>
  - Body context: «…<the 1-2 lines around the key match>…»

Then call AskUserQuestion with options:

• Post — proceed to Step 11.
• Edit — ask for free-text instructions (e.g. "shorten the summary", "drop the perf section", "include #N" / "include all Tier 3" to promote candidates, "add a note about backport"), apply, and loop back to the preview.
• Cancel — go to Step 12.

If --dry-run was set, skip the question and jump to Step 12 with cancel semantics, printing a notice that the recap was not posted. The Tier 3 block is still printed in dry-run.

Step 11: Post the comment

Call the Post comment tool (see references/runtime-tooling.md) with the rendered markdown from Step 9 (including the attribution footer). Do not set visibility/commentVisibility — this is a regular comment.

POST-action verification: re-fetch the Epic (Cursor: comment_limit=5; Claude Code: fields: ["comment"]) and confirm the new comment is present (match the footer string Generated by create-epic-recap). If verification fails, surface the error and do not retry automatically.

On success, print:

Recap posted: https://datadoghq.atlassian.net/browse/<EPIC-KEY>

Step 12: Save and exit (when not posting)

When the user picks Cancel or --dry-run was specified:

1. Write the final markdown to /tmp/<EPIC-KEY>-recap.md.
2. Print the path, e.g. Saved draft to /tmp/OTAGENT-820-recap.md.
3. Exit cleanly.

Errors and edge cases

• Atlassian MCP auth failure — stop, do not try a different transport, ask the user to authenticate.
• Claude Code cloudId errors — see references/runtime-tooling.md (use getAccessibleAtlassianResources to resolve the UUID).
• jira_get_issue_development_info 500/empty — the dev-status endpoint is fragile (no parallel calls, occasional downtime, exact CamelCase "GitHub"). On failure, A2 + Phase B carry the pipeline; on Rovo this tool doesn't exist at all. Full handling in references/pr-discovery.md.
• gh not authenticated — gh auth status; if it fails, ask the user to gh auth login.
• gh search prs rate-limited — back off 60 s, retry once; if still failing, ask for manual PR URLs (Step 4 fallback).
• Very large PR set (> 25 PRs) — present a numbered list and ask via AskUserQuestion whether to include all or narrow by date / label / repo.
• Posting fails — keep the markdown on disk (Step 12 path), report the error verbatim, do not silently retry.

Important constraints

• Never post without explicit user approval — --dry-run and Cancel must result in no Jira write.
• Never modify the Epic description or other fields — comments only.
• Always include the attribution footer so future readers know the recap was AI-generated.
• Never include secrets, internal-only URLs, or sensitive customer data. Mask customer names as <customer> and flag during preview.
• Comment body is markdown — pass it directly; do not pre-render to ADF/Wiki. On Cursor jira_add_comment takes Markdown in body; on Claude Code pass commentBody with contentFormat: "markdown".