OpenTag connects Slack with coding agents running on a computer controlled by the user, so channel mentions can queue work and return status, decisions, and evidence in the same thread. It is for engineering teams that want to run agents such as Claude Code, Codex, or Cursor against local checkouts through the Agent Client Protocol.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add amplifthq/opentag --skill debugging-ci-failuresgit clone --depth 1 https://github.com/amplifthq/opentagWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amplifthq/opentag/debugging-ci-failures)<a href="https://agentmods.dev/skills/amplifthq/opentag/debugging-ci-failures"><img src="https://agentmods.dev/badge/skills/amplifthq/opentag/debugging-ci-failures/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/amplifthq/opentag/debugging-ci-failures"><img src="https://agentmods.dev/badge/skills/amplifthq/opentag/debugging-ci-failures.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00189 | $0.04399 |
| Opus 5.5 | $0.00076 | $0.01760 |
| Sonnet 5.5 | $0.00038 | $0.00880 |
| Haiku 4.5 | $0.00019 | $0.00440 |
Grade A, and why
debugging-ci-failures scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to debugging-ci-failures — 91 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging PostHog CI failures
Before you propose a change to CI, check things already tried for the idea. It records what was measured, and why some good-sounding changes were reverted or rejected.
Find the first meaningful failure, classify it, reproduce the smallest useful case locally when appropriate, and report the result. Avoid public-visible or irreversible actions unless the user explicitly asks.
Always start with the hogli ci:insights digest. It aggregates across runs and
branches, which gh cannot do cheaply, and tells you whether a failure is
likely trunk-borne, gate-only, or isolated to a small set of branches. gh is
authoritative for one run's current state and attribution. Use the digest to
decide what to inspect; use gh to confirm whose failure it is and exactly
what failed in a given run.
This skill triages and classifies. Once a failure is confirmed flaky, hand off
to the fixing-flaky-tests skill, which owns local reproduction, root-cause
fixing, and N-run validation. For "who broke master" — the culprit commit and
the commit that fixed it — hand off to the investigating-ci-failures skill,
which owns the green/red boundary analysis. For aggregate pipeline health (is CI
getting slower, which workflow is the long pole, how long PRs take to merge),
read diagnosing-ci-and-merge-bottlenecks. Both are product skills under
products/engineering_analytics/skills/, not invocable here: read the
SKILL.md at that path.
Rule out a platform outage first
GitHub Actions goes down often enough that it belongs before any log reading, and
a platform incident makes every other signal a symptom. It costs two page loads:
https://www.githubstatus.com/ for GitHub, https://status.depot.dev/ for the
runners. Check them whenever failures are broad — several workflows at once, a
burst of runs failing together, jobs dying before Checkout, or anything red
across unrelated PRs.
Report an outage as an outage, name the component, and stop recommending reruns.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 325 lines · 189 tokens per session scan A 1031d9cbbc41
debugging-ci-failures is a skill published in the GitHub repository amplifthq/opentag (0 stars, last pushed 2d ago), licensed MIT. It adds 189 tokens to every session and 4,399 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to debugging-ci-failures, differing in 91 lines, and is treated as a copy.
Other skills, from other repositories
ci-troubleshoot
Diagnose failed GitHub Actions runs for pi-agent-dashboard: the 11-file workflow taxonomy, affected-test selection, the release pipeline, known failure modes, and how to read gh run logs and retrigger jobs. Use when a CI run is red, a release is stuck, a workflow won't dispatch, or you need to know which workflow does…
fix-ci-failures
Investigate and fix CI failures on a pull request. Use when CI checks fail on a PR branch — covers finding the PR, identifying failed checks, downloading logs and artifacts, extracting the failure cause, and iterating on a fix. Requires the gh CLI.
debug
Reproduce, minimize, localize, identify root cause, and distinguish diagnosis from an authorized fix. Prefer root-cause over symptom patches.
feedback
Skill "feedback" from Hmbown/Codewhale, covering feedback, when to use, setup, workflow and non-goals.
operating-github-ci-fixer
Use when the user asks OpenSRE to fix failing GitHub CI, GitHub Actions checks, failing pull request checks, a broken PR branch, or CI on a named branch such as main.
oversight-team
Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention…