Developer Tools
GitHub
Connect your AI to GitHub
Engineering managers, DevOps leads, and product managers need visibility into pull requests, workflow failures, and issue backlogs scattered across dozens of repositories. AI can synthesize that data instantly, but only if it sees relevant fields instead of and raw API payloads.
Why it matters
Capabilities that matter most for GitHub
01
Clean fields, not raw payloads
Pull requests, workflow runs and issues become lists with labelled fields, so AI reads what matters instead of thousands of tokens of JSON.
02
Code beside the work it serves
Join GitHub issues and pull requests with Jira, helpdesk or CRM lists to trace a customer request to the code that fixed it.
03
Governed AI for engineering
AI tools answer within per-user and per-team security across every repository you connect.
Use cases
Example AI Use Cases for GitHub
scenario
-
After a deploy, you want to know whether it introduced new errors, but GitHub knows the run and commit while Bugsnag knows the releases and errors. Matching them by date alone pulls in old errors that simply appeared on a new version.
What popdock provides
--
Give a GitHub Actions run, or use the latest successful run on a branch, and it records the workflow, commit, author and message. It finds the Bugsnag releases built from that exact commit in each project, then lists the errors first seen after each release, with severity, events and links. You get session counts, errors introduced and a one-line verdict: clean, new handled errors only, or new unhandled errors. It only reads, and if no release carries the commit it says the build was not reported rather than matching by date.
Business outcome
---
You know within minutes whether a deploy broke something, tied to the exact commit that shipped.
scenario
-
Errors get marked fixed in Bugsnag while their GitHub issues stay open, so the backlog overstates what is broken. Closing them by hand means matching each error to its issue, and closing on a title that only looks similar shuts the wrong issue.
What popdock provides
--
Name the Bugsnag project and a look-back period, and it lists errors marked fixed in that window and looks for open GitHub issues carrying each exact error id in the title, never a loose title match. You see the pairs with issue numbers and assignees and choose which to close. For each one you confirm, it posts a comment with the Bugsnag link and last seen date, then closes the issue without changing anything else. If a comment posts but the close fails, it reports the issue as still open and does not comment again.
Business outcome
---
Your GitHub backlog reflects what is actually still broken, without wrongly closed issues.
scenario
-
Filing a GitHub issue for a Bugsnag error means copying severity, users affected, release stages and recent occurrences into the body, then linking the issue back in Bugsnag. Skip the search first and you end up with two issues for one error.
What popdock provides
--
Give the error, and optionally a label, and it reads the error and its five most recent occurrences, then checks GitHub for an issue with that error id and for open issues about the same error class, asking whether one already covers it. You see the exact title, label and body before anything is created. After your yes it creates one issue, reads back its number and link, and links it to the error in Bugsnag. If the read-back or the link fails it reports where things stand and never creates the issue twice.
Business outcome
---
Each Bugsnag error gets one well documented GitHub issue, linked in both directions.
scenario
-
When a release introduces new errors, the culprit is usually among the commits shipped since the previous release. Finding it means listing those commits from GitHub and reading them against each new error, which is slow and invites guesswork.
What popdock provides
--
Name the Bugsnag project and app version, and it finds that release and the one before it in the same stage. It lists errors first seen in the new release and the GitHub commits in between, using the source revisions as boundaries where available. For each new error it names commits whose message mentions its class, context or file as leads, never as findings. It only reads, uses a seven-day window when there is no previous release, and says when the range is too wide to attribute.
Business outcome
---
Engineers get a short list of suspect commits for each regression instead of reading the whole release.
scenario
-
Some open Bugsnag errors have a GitHub issue, some have none, and some point at an issue that was closed while the error kept happening. Sorting that out means searching GitHub error by error, keeping track of what you found, and deciding which loose title matches to trust.
What popdock provides
--
Name the Bugsnag project, and optionally a severity, and it reads the open errors, busiest first, and looks for GitHub issues carrying each error id, whether open or closed. Where none is found it tries the error class and marks any hit as a probable match only. You get three tables, tracked by an open issue, tracked by a closed issue, and untracked, with counts, and an error matching several issues shows every match. It only reads and suggests next steps without filing or closing anything.
Business outcome
---
You see which errors are tracked, which have fallen through, and which issues were closed too early.
scenario
-
When a release misbehaves you want the exact commit and build behind it, but Bugsnag stores a revision that may be short or full, and GitHub has many runs around the same time. Matching them by date alone risks blaming the wrong build and sending engineers to read the wrong code.
What popdock provides
--
Name the Bugsnag project, and optionally an app version, and it reads the release with its stage, build label, revision and health figures. The revision is carried to GitHub to find the commit, coping with short revisions, and then the workflow runs built from that commit. You get the release, commit, workflow runs and health side by side. It only reads, asks which release you mean when several stages match, and reports a commit without a run rather than guessing a nearby one.
Business outcome
---
Every release is traceable to the commit and build that produced it, in a single step.
scenario
-
A GitHub issue for an error goes stale when nobody updates it with how often the error is still happening. Pulling counts by version and stage from Bugsnag and writing them up is tedious, so the issue keeps describing last month's problem.
What popdock provides
--
Give the error, and optionally the issue number, and it reads the error and the last seven days of events, counting occurrences by app version and release stage and how many were unhandled. It finds the GitHub issue, asks which one if several match, and checks whether a similar update was posted in the last day. You see the draft comment, and it posts once only after your yes. If the issue is closed while the error is still being seen, it points that out and leaves reopening to you.
Business outcome
---
Issues stay current with real error activity, so priorities are set on this week's numbers.
scenario
-
After a deploy goes out, someone has to compare Ghost Inspector results before and after it to see what broke. Lining up test times against the GitHub workflow run, and then working out what shipped, is slow, so after a busy release it is often skipped.
What popdock provides
--
Name the GitHub workflow, and optionally a branch, and it finds the latest successful run and the one before it to set the windows. Ghost Inspector results either side are compared per test as regressed, fixed, still failing or not run since. For each regression it reads the failing step, and it lists the GitHub commits that shipped in the deploy. It ends with a verdict and says when the deploy was untested. Nothing in Ghost Inspector or GitHub is changed.
Business outcome
---
You know within minutes whether a deploy broke anything, and which commits shipped with it.
scenario
-
Deciding whether a release is ready means weighing the open GitHub issues on its milestone against the latest Ghost Inspector results, usually in a meeting with two browser tabs. Stale test results and test-failure issues that are already fixed muddy the call.
What popdock provides
--
Name the GitHub milestone, and optionally a suite filter, and it reads the milestone and its open issues alongside the most recent Ghost Inspector result for each matching suite. Open issues about test failures are checked against the current suite result, so a fixed failure reads as a close candidate, not a blocker. Results older than the latest closed issue are marked stale and suites with no results count as a risk. You get a Ready, Ready with risks or Not ready verdict with its reasons. It reads from Ghost Inspector and GitHub but writes nothing back.
Business outcome
---
Release decisions rest on current test evidence and the real open work, summarized in one verdict.
scenario
-
Turning a failed Ghost Inspector suite run into a GitHub issue means copying failing steps and console errors for each test, checking for an issue already open, and linking the two. Skip the check and the backlog fills with duplicate issues for the same broken suite.
What popdock provides
--
Give the suite result and an optional label, and it reads the failed run with each failing test's step and first console errors. It looks for an open GitHub issue already tracking that suite, and drafts either a new issue or a comment on the existing one. Nothing is written until you confirm the draft, and each system is written at most once. The suite result then gets a comment linking the GitHub issue, and if the new issue cannot be found again it does not create a second one.
Business outcome
---
Every suite failure is tracked in GitHub once, with the evidence attached and a link back from Ghost Inspector.
scenario
-
GitHub issues about Ghost Inspector failures stay open long after the tests pass again, while new failures go unfiled. Sorting it out means checking every open issue against current results, and every failing suite against the backlog, by hand.
What popdock provides
--
Give a title marker and a lookback period, and it reads open GitHub issues carrying that marker and checks each suite and named test in Ghost Inspector. Issues are sorted into now passing, still failing and not found, with ambiguous suite names listed rather than guessed. It then finds suites failing recently with no open issue. It ends with four tables, one per group, with the latest result times. Closing and filing are recommended, never done.
Business outcome
---
Your issue backlog matches reality: stale issues ready to close and real failures nobody has filed yet.
scenario
-
After a GitHub Actions deploy, someone should run the Ghost Inspector smoke suite against it, but first they need to know the build finished and passed. Running tests against a half-finished or failed deploy wastes runs and produces misleading failures that send people chasing the wrong problem.
What popdock provides
--
Name the workflow, optionally a branch, and the suite, and it checks the latest GitHub run has completed successfully, stopping if it is still running or failed. It shows which run and suite you are about to test, warns when the suite's start URL does not look like the deployed environment, and waits for confirmation. The suite runs once and failing tests are named. A one-line verdict says whether that commit passes.
Business outcome
---
Every deploy gets a smoke test only when the build is truly done, with a clear pass or fail verdict.
scenario
-
When a Ghost Inspector test starts failing, the first question is what changed. Answering it means finding the last pass and first failure, then listing GitHub commits and workflow runs in that gap, while allowing for the time zone differences between the two systems.
What popdock provides
--
Name the test and it finds the start of its current failing streak and the last pass before it. It lists the GitHub commits and workflow runs between those two times, comparing in UTC and widening the window when a time zone is missing. Commits whose messages mention the page or feature the test covers are marked as likely suspects, labelled as inference. When no commit falls in the window it points to data, environment or the test itself. It never claims a commit caused the failure and changes nothing.
Business outcome
---
You get a short list of suspect commits for a broken test, with correlation kept separate from cause.
scenario
-
Closing a GitHub issue about a test failure should rest on a fresh Ghost Inspector run, but finding the right test from the issue, running it and recording the result takes several steps. Issues get closed on a hunch, or sit open for weeks after the fix has shipped.
What popdock provides
--
Give the GitHub issue number, and optionally a test ID, and it reads the issue and its comments to find the Ghost Inspector test, stopping if none or several match. Nothing runs until you confirm the test, and it runs once. The result, with the failing step if it failed, is posted as one comment on the issue. A pass suggests the issue can close, but it never closes it, and it warns that one pass is weak evidence for a test known to be flaky.
Business outcome
---
Issue fixes are verified with a fresh test run and the evidence recorded right on the issue.
scenario
-
GitHub Actions runs get stuck queued or in progress for hours, holding runners and blocking later builds. Clearing them means finding the old ones, checking whether a newer run already replaced each, and cancelling them one by one.
What popdock provides
--
Name the repository and an age threshold, and it finds runs queued or in progress longer than that. Each is checked for a newer run of the same workflow on the same branch, and long-running deploy or release workflows are pointed out before you choose. Nothing is canceled unless you explicitly approve it, the default is none, and each approved run is canceled once and re-read to report its final status. A failed cancel is reported and the rest carry on.
Business outcome
---
Stuck runs are cleared safely, with superseded runs identified and deploys protected from accidental cancellation.
scenario
-
Closing a GitHub milestone means finding the issues still open on it, moving them to the next milestone one at a time and then closing the milestone. Done in a rush at the end of a sprint, unfinished work gets closed by mistake or stranded on a finished milestone.
What popdock provides
--
Name the milestone and optionally the next one, and it reads both, lists the open issues with assignees and shows the plan. You confirm, and can leave out individual issues, before anything moves. Approved issues move to the next milestone, the milestone closes, and the final counts are read back. It never closes issues, never creates the next milestone and never edits the milestone's title or dates.
Business outcome
---
Milestones close cleanly with unfinished work carried forward, not lost or closed by mistake.
scenario
-
When GitHub Actions turns red across a repository, the question is which commit started each failure, and whether it is a real break or a flaky workflow. Answering it means walking run histories branch by branch to find the last success and the commit that came after it.
What popdock provides
--
Name the repository, a lookback period and optionally a workflow, and it groups failed runs by workflow and branch. For each it finds the last success and the run that broke after it, reads that commit's author and message, and checks whether the workflow is still active. Pairs already fixed, or failing and passing on the same commit, are labelled rather than chased. It never invents an error message, pointing you to the run log instead. Nothing in GitHub is changed.
Business outcome
---
You know which commits broke which builds, and which failures are just flaky, before anyone starts digging.
scenario
-
Knowing whether GitHub milestones will land on time means checking each one's open and closed counts, due date and the issues still open, then spotting the ones with no owner or no recent activity. It turns into a manual status meeting every week.
What popdock provides
--
Name the repository, and optionally one milestone, and it reads each open milestone with its open work. It calculates percent complete, marks milestones overdue, at risk or unscheduled using stated rules, and flags open issues with no assignee or no update in 14 days. You get a status table, the issues holding up each risky milestone and a short summary of decisions needed, with the date used for judging stated. It reads from GitHub but writes nothing back.
Business outcome
---
Milestones at risk are visible early, with the specific issues and decisions that would bring them back on track.
scenario
-
A broken GitHub Actions workflow should be tracked as an issue, but each person who notices it opens a new one or assumes someone else did. Gathering the failing run, the suspect commit and the length of the failure streak takes time nobody has mid-sprint.
What popdock provides
--
Name the workflow and optionally a branch, and it confirms the latest run actually failed and counts the consecutive failures since the last success. It looks for an open issue already tracking the workflow and drafts either a comment with the new run or a new issue with the first and latest failures and the suspect commit, proposing only labels already in use. You confirm the draft before anything is written, once. It links run pages rather than guessing at errors.
Business outcome
---
Every CI break is tracked by exactly one issue, kept current with the latest failing run.
scenario
-
Re-running a failed GitHub Actions workflow is the reflex fix, but often a newer run already passed, one is already queued, or the same commit has failed three times and is not flaky at all. Blind re-runs waste runner minutes and hide real breaks behind a green tick.
What popdock provides
--
Name the workflow and optionally a branch, and it checks the workflow is active and finds its latest failed run. It declines when a newer run succeeded or is already queued, and warns that a commit failing three or more times needs a code fix. You see the run details and confirm before it re-runs, and it re-runs one run at most, never in a loop, then reports the new status. If the workflow name matches nothing it lists close matches.
Business outcome
---
Re-runs happen only when they can help, so real failures get fixed instead of retried.
scenario
-
A team lead wants to know what happened in a GitHub repository this week, but that means reading commit logs, issue lists and CI results separately. The summary either takes an hour of clicking or, more often, does not happen at all.
What popdock provides
--
Name the repository and a lookback period, and it reads commits, issues opened and closed and workflow runs in that window. Commits are counted by author and grouped into themes, and each workflow gets a success rate with those under 80 percent called out. You get headline numbers, top themes, notable issues and CI health in a digest readable in two minutes, with the exact date range stated and empty sections shown as no activity. No record in GitHub is touched.
Business outcome
---
Team leads get a two-minute weekly picture of a repository without reading raw logs.
scenario
-
Triaging a new GitHub issue means reading the thread, choosing labels that actually exist, finding who has handled similar issues and picking a milestone. Invented labels, wrong assignees and stale milestones creep in when it is done quickly between other work.
What popdock provides
--
Name the issue and it reads it and its comments, then gathers the labels already in use, who closed similar issues and the open milestones. You see current and proposed labels, assignee and milestone, each with a one-line reason, and apply only what you approve. It never invents a label, never edits the title, body or state, and asks first if the issue is closed. Labels and assignee are added only where missing, and the final state is read back.
Business outcome
---
New issues get the right labels, owner and milestone in one reviewed step.
scenario
-
A GitHub repository with hundreds of open issues hides the ones that need attention now: busy threads with no owner, blocked work and issues waiting on a reporter. Working through them by hand rarely gets past the first page of results.
What popdock provides
--
Name the repository, a lookback period and optionally a label, and it reads open issues and scores each on missing assignee, milestone and labels, discussion volume and age. For the top ten it reads the latest comments to say whether each is blocked, waiting on the reporter, needs an owner or is in progress, saying so when the comments are unclear. You get counts and a recommended order of work. It is read-only in GitHub.
Business outcome
---
You know which open issues to pick up first, judged from what the threads actually say.
scenario
-
When a GitHub Actions build fails on a feature branch, the Jira issue for that work rarely hears about it. Linking them means spotting the Jira key in the branch or commit message, opening the issue in Jira and pasting the run details by hand.
What popdock provides
--
Name the repository, the Jira project and a lookback period, and it finds the newest failed run per branch and reads a Jira key from the branch, then the commit message. Each key is confirmed in Jira, closed issues are skipped, and issues already commented with that run are skipped so nothing posts twice. You see the proposed comments and unmatched runs, and nothing is posted until you confirm. Each comment names the workflow, branch, run, commit and author.
Business outcome
---
Jira issues learn about their failed builds automatically, without duplicate comments or noise on closed work.
scenario
-
Escalating a GitHub issue into Jira means creating the ticket, adding a link back to GitHub and noting the Jira key on the GitHub issue. Skip the check and the same issue gets escalated twice by different people on different days.
What popdock provides
--
Give the GitHub issue number and the Jira project, and it reads the issue and checks Jira for an existing link to it, stopping if there is one and listing similar summaries for you to judge. It resolves the project and issue type and shows the Jira summary, and nothing is created until you confirm. It creates the issue once, links it to GitHub and comments the Jira key on the GitHub issue, naming any step that failed. It says that the GitHub body is not copied.
Business outcome
---
GitHub issues reach Jira once, linked both ways, so engineering and planning track the same work.
scenario
-
Work that lands in GitHub without a Jira key is invisible to planning and reporting, and commits that cite a key that does not exist mislead everyone. Checking means reading every commit message and validating each key against Jira by hand.
What popdock provides
--
Name the repository, the Jira project and a lookback period, and it reads commits, ignoring merges, and pulls out the Jira keys. Each key is checked against the project to separate tracked commits, untracked commits and dangling references to issues that do not exist. Untracked commits are grouped by author with their Jira user. If no commit carries any key it says the team may not use keys rather than flagging everything. Nothing is created.
Business outcome
---
Untracked work and broken Jira references come to light, so planning reflects what was actually built.
scenario
-
Jira says an issue is Done but no code references it, or it is still To Do while commits pile up against it, or it has sat In Progress with no commits for weeks. Spotting these mismatches means comparing every issue's status with the GitHub commit log by hand, issue by issue.
What popdock provides
--
Name the Jira project, the repository and a lookback period, and it reads recently updated Jira issues and the GitHub commits for the same window in one pass. Keys are matched exactly so ABC-12 never matches ABC-123. Each issue is classed as Done with no commits, not started with commits, stalled in progress or consistent, by the meaning of its status, and statuses it cannot place are listed. Truncated commit lists are flagged. Status changes are suggested, never made.
Business outcome
---
Board status matches the code, with stalled and mislabelled issues called out for the next stand-up.
scenario
-
Teams agree that GitHub issues with a certain label should be tracked in Jira, then copy them across by hand when they remember. Some are missed entirely, others are copied twice, and the links between the two systems are rarely added.
What popdock provides
--
Name the label, the repository and the Jira project, and it reads open GitHub issues with that label and checks every existing GitHub link in Jira to find the ones not yet synced. You see the gap and confirm all or some of them before anything is created, up to 20 per run. Each gets one Jira issue, a link back to GitHub and a comment with the Jira key. If the link check fails it stops, since that is what prevents duplicates.
Business outcome
---
Labelled GitHub issues reach Jira reliably, each exactly once, with links in both directions.
scenario
-
Answering what code exists for a Jira issue means searching GitHub branches, commits, issues and workflow runs for its key, then checking who wrote the code. Partial key matches, and a Done issue whose branch build is failing, are easy to miss along the way.
What popdock provides
--
Give the Jira issue key and it reads the issue, then finds every GitHub branch, commit and issue that references the key exactly, discarding partial matches, plus each branch's latest workflow runs. Commit authors are mapped to Jira users. Mismatches are called out, such as a Done issue whose branch build is failing, a To Do issue with commits, or commits that exclude the assignee. No references is reported as a result, not an error. Nothing in GitHub or Jira is changed.
Business outcome
---
You see all the code behind a Jira issue in one view, with status mismatches flagged.
scenario
-
Once BugHerd tasks and GitHub issues are linked, they drift. A developer closes the issue but the BugHerd task stays open, or the site is signed off in BugHerd while the issue lingers on GitHub. Finding these means checking every linked pair in both tools by hand.
What popdock provides
--
Name the BugHerd project, the GitHub repository and a lookback period, and it collects links from both sides: BugHerd tasks, including archived ones, that reference an issue, and GitHub issues whose titles carry a BugHerd task number. It reads the other side of each link and sorts the pairs into closed on GitHub but open in BugHerd, done in BugHerd but open on GitHub, and broken links, with the pairs in step given as a count. It is read-only, so nothing is changed in either system.
Business outcome
---
A clear list of drifted BugHerd and GitHub pairs to act on, with nothing changed until you decide.
scenario
-
Clients and reviewers report bugs in BugHerd, but developers work from GitHub issues. Moving a task across by hand means copying the description, page, severity and reporter, adding a link back, and remembering whether it was already sent, so the same bug often ends up as two issues.
What popdock provides
--
Name the BugHerd project, the task number and the GitHub repository, and optionally an assignee, and it reads the task and checks GitHub for an issue already opened for it. If one exists, it reports that issue and stops. It drafts an issue titled with the BugHerd project and task number, with the description and a list of page, severity, requester, reported date and the task link, labelled bug. Nothing is opened until you confirm, then it creates the issue once, reads back its number and link, and comments that link on the BugHerd task.
Business outcome
---
BugHerd reports reach developers in GitHub once, with full context and a link both ways.
scenario
-
Linked BugHerd tasks and GitHub issues fall out of step all the time. Fixing them means closing issues one by one on GitHub, marking tasks done in BugHerd and leaving a note on each so people know why, and doing it in bulk by hand risks closing the wrong item.
What popdock provides
--
Name the BugHerd project and the GitHub repository, and it finds linked pairs where exactly one side is finished. For issues closed on GitHub it proposes marking the BugHerd task done with a comment asking for a check on the site. For tasks done in BugHerd it proposes commenting on and closing the GitHub issue. Every proposal appears in one table and only the rows you approve are applied. It never reopens anything or changes titles, labels, assignees or milestones, and if one update fails it reports the error and carries on.
Business outcome
---
BugHerd and GitHub agree again, with a comment explaining each change and only the changes you approved.
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