Official agent skill

Create Epic Recap

by DataDog in DataDog/datadog-agent

A skill your agent uses 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…

OfficialApache-2.0Auto-check: notesDevelopment

Install Create Epic Recap

skills CLI
$ npx skills add DataDog/datadog-agent --skill create-epic-recap -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install DataDog/datadog-agent create-epic-recap --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/create-epic-recap .claude/skills/create-epic-recap && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
create-epic-recap
GitHub stars
3.8k
Token cost
~5k tokens
SKILL.md length
2,424 words
Files
4 (incl. references)
Skills in repo
35
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses 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…

  • Works in 12 steps: Parse arguments → Fetch the Epic → Fetch child issues → …
  • Manager asks to recap
  • SKILL.md covers Reference files (load as needed), Prerequisites, Example and Step 1: Parse arguments, plus 13 more sections
  • Calls gh; reaches datadoghq.atlassian.net

What it does

Create Epic Recap is an agent skill from DataDog/datadog-agent, published by the product's own GitHub organization. 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.

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `recap-template.md`, `references/pr-discovery.md` and `references/runtime-tooling.md`).

It sits in Development, covering Changelog and release notes. It works with GitHub, Jira, Datadog and Model Context Protocol. The repository describes itself as: Main repository for Datadog Agent. The licence is Apache-2.0.

When your agent uses it

  • Manager asks to recap
  • Post an update on a Jira Epic — a progress update for an in-progress Epic (how far along it is
  • Whats shipped so far
  • A resolution recap for a finished one

Example prompts

  • “s shipped so far, what”
  • “/create-epic-recap”

Requirements

  • Pre-approved tools (allowed-tools): Bash, Read, Write, Glob, Grep, AskUserQuestion, mcp__atlassian__getJiraIssue, mcp__atlassian__searchJiraIssuesUsingJql, mcp__atlassian__getJiraIssueRemoteIssueLinks, mcp__atlassian__addCommentToJiraIssue, mcp__atlassian__getAccessibleAtlassianResources

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Parse arguments
  2. Fetch the Epic
  3. Fetch child issues
  4. Find merged PRs
  5. Fetch PR details
  6. Read release notes from the PRs
  7. Classify the change
  8. Ask the user for the remaining sections
  9. Render the recap
  10. Preview and approval
  11. Post the comment
  12. Save and exit (when not posting)

What it can do on your machine

Read from SKILL.md and the folder at commit 20eff25. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Glob
    • Grep
    • AskUserQuestion
    • mcp__atlassian__getJiraIssue
    • mcp__atlassian__searchJiraIssuesUsingJql
    • mcp__atlassian__getJiraIssueRemoteIssueLinks
    • mcp__atlassian__addCommentToJiraIssue

    …and 1 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • datadoghq.atlassian.net

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Create Epic Recap loads about 5k tokens when it runs, and up to ~9k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 2,424 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Glob, Grep, AskUserQuestion, mcp__atlassian__getJiraIssue, mcp__atlassian__search

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DataDog/datadog-agent at commit 20eff25, republished under its Apache-2.0 licence (© DataDog). 2,424 words, ~5,017 tokens.

Download SKILL.mdSave it as .claude/skills/create-epic-recap/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
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.
allowed-tools
Bash, Read, Write, Glob, Grep, AskUserQuestion, mcp__atlassian__getJiraIssue, mcp__atlassian__searchJiraIssuesUsingJql, mcp__atlassian__getJiraIssueRemoteIssueLinks, mcp__atlassian__addCommentToJiraIssue, mcp__atlassian__getAccessibleAtlassianResources
argument-hint
<EPIC-KEY e.g. OTAGENT-820> [--dry-run]
model
sonnet

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):

bash
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:

bash
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:

bash
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.

Show full SKILL.md (977 more words)Show less

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}}:

PlaceholderSource
{{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".

© DataDog, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in .agents/skills/create-epic-recap of DataDog/datadog-agent.

  • SKILL.md
  • recap-template.md
  • references/pr-discovery.md
  • references/runtime-tooling.md

Open the folder on GitHubat commit 20eff25

Compare with similar skills

Create Epic Recap next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Create Epic Recap compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Create Epic Recap this skillDataDog/datadog-agent3.8k—~5kAutomated safety check: NotesApache-2.0
Review Release Noteschef/chef-web-docs143—~5.2kAutomated safety check: PassCustom licence
Plane Release Notes Generatormakeplane/plane61k—~2.5kAutomated safety check: PassAGPL-3.0
Release Prepjohnhuang316/code-index-mcp1k—~680Automated safety check: PassMIT
Git Releasewesammustafa/opencode-primer3971 repos~409Automated safety check: PassMIT
Store Submitzhitongblog/solomd1.2k—~1.7kAutomated safety check: NotesMIT

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  • Follow PR

    DataDog/datadog-agent

    Official

    Monitor the current PR's GitLab pipeline to completion, then report success, auto-fix, or investigate a failure.

    3.8k GitHub stars~3.2k tokensUpdated today
    Auto-check passed
  • Explain Lading Config

    DataDog/datadog-agent

    Official

    Explains a lading.yaml config file from the regression test suite, using the lading Rust source as ground truth for field meanings and defaults.

    3.8k GitHub stars~1.2k tokensUpdated today
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  • Distill

    DataDog/datadog-agent

    Official

    Extract an Allium specification from an existing codebase. An agent skill from DataDog/datadog-agent.

    3.8k GitHub starsUsed in 1 repo~7k tokens
    Auto-check passed
  • Run E2E

    DataDog/datadog-agent

    Official

    Run one already-written new-e2e test locally and triage the setup failures that stop it — "run the containers e2e tests", "my e2e run fails before any test starts".

    3.8k GitHub stars~2.2k tokensUpdated today
    Auto-check: notes

Questions about Create Epic Recap

What does Create Epic Recap do?

A skill your agent uses 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…. Create Epic Recap is an agent skill from DataDog/datadog-agent, published by the product's own GitHub organization. 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.

When should I use Create Epic Recap?

Create Epic Recap fits situations like: manager asks to recap; post an update on a Jira Epic — a progress update for an in-progress Epic (how far along it is; whats shipped so far; A resolution recap for a finished one.

How do I install Create Epic Recap in Claude Code?

Run `npx skills add DataDog/datadog-agent --skill create-epic-recap -a claude-code`. Or copy the skill folder (.agents/skills/create-epic-recap in DataDog/datadog-agent) into .claude/skills/create-epic-recap in your project. Claude Code loads it when a task matches its description.

How do I install Create Epic Recap in Codex?

Run `npx skills add DataDog/datadog-agent --skill create-epic-recap -a codex`. Or copy the skill folder (.agents/skills/create-epic-recap in DataDog/datadog-agent) into .agents/skills/create-epic-recap in your project. Codex loads it when a task matches its description.

Can I use Create Epic Recap in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DataDog/datadog-agent --skill create-epic-recap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/create-epic-recap, .gemini/skills/create-epic-recap, .github/skills/create-epic-recap and .opencode/skills/create-epic-recap in your project.

What does Create Epic Recap need to run?

Going by SKILL.md and its folder, Create Epic Recap needs the command-line tools its instructions call (gh). Its frontmatter pre-approves these tools: Bash, Read, Write, Glob, Grep, AskUserQuestion, mcp__atlassian__getJiraIssue, mcp__atlassian__searchJiraIssuesUsingJql, mcp__atlassian__getJiraIssueRemoteIssueLinks, mcp__atlassian__addCommentToJiraIssue, mcp__atlassian__getAccessibleAtlassianResources.

Does Create Epic Recap access the network?

SKILL.md names 1 domain. In commands or code: datadoghq.atlassian.net; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Create Epic Recap safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Create Epic Recap use?

Create Epic Recap is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Create Epic Recap use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.9k tokens, read only when the agent opens those files.

What are the alternatives to Create Epic Recap?

Skills that share tags, products or a category with Create Epic Recap: Review Release Notes (chef/chef-web-docs, 143 stars), Plane Release Notes Generator (makeplane/plane, 61k stars), Release Prep (johnhuang316/code-index-mcp, 1k stars) and Git Release (wesammustafa/opencode-primer, 397 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Create Epic Recap?

DataDog (a GitHub organization, an official publisher) maintains it in DataDog/datadog-agent, which has 3,757 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 8, 2026.

Source: DataDog/datadog-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.