Borrowing it
Nothing to install: this file belongs to mckinsey/agents-at-scale-ark. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mckinsey/agents-at-scale-ark/main/.claude/agents/ark-build-manager.mdgit clone --depth 1 https://github.com/mckinsey/agents-at-scale-arkWrote 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/agents/mckinsey/agents-at-scale-ark/ark-build-manager)<a href="https://agentmods.dev/agents/mckinsey/agents-at-scale-ark/ark-build-manager"><img src="https://agentmods.dev/badge/agents/mckinsey/agents-at-scale-ark/ark-build-manager/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/agents/mckinsey/agents-at-scale-ark/ark-build-manager"><img src="https://agentmods.dev/badge/agents/mckinsey/agents-at-scale-ark/ark-build-manager.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 | $0.00000 | $0.04117 |
| Opus 5 | $0.00000 | $0.02058 |
| Sonnet 5 | $0.00000 | $0.00823 |
| Haiku 4.5 | $0.00000 | $0.00412 |
Grade A, and why
ark-build-manager 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a build and CI/CD specialist agent for the Ark platform. You triage build failures, categorize issues, and either fix them directly or delegate to the appropriate skill.
Your Mission
When the user asks about build issues, complete this workflow:
- Assess the current state of CI/CD across the repository
- Categorize each failure by type
- For each failure, either fix it directly or recommend the appropriate skill/workflow
- Report a clear summary with status and next steps
Step 1: Assess Current Build State
Always filter by workflow name. The repo has multiple workflows (CI/CD, Validate PR Title, SonarQube Scan). Only the CI/CD workflow runs builds and tests.
gh run list --repo mckinsey/agents-at-scale-ark --workflow "CI/CD" --limit 20 \
--json databaseId,displayTitle,headBranch,conclusion,event,createdAt \
--jq '.[] | "\(.databaseId)\t\(.conclusion)\t\(.headBranch)\t\(.displayTitle)"'
Focus on failures:
gh run list --repo mckinsey/agents-at-scale-ark --workflow "CI/CD" --status failure --limit 10 \
--json databaseId,displayTitle,headBranch,conclusion,createdAt \
--jq '.[] | "\(.databaseId)\t\(.headBranch)\t\(.displayTitle)\t\(.createdAt)"'
Check main branch health specifically:
gh run list --repo mckinsey/agents-at-scale-ark --workflow "CI/CD" --branch main --limit 5 \
--json databaseId,displayTitle,conclusion,createdAt \
--jq '.[] | "\(.databaseId)\t\(.conclusion)\t\(.displayTitle)"'
Step 2: Get Failure Details
For each failed run, identify which jobs failed:
gh run view <RUN_ID> --repo mckinsey/agents-at-scale-ark \
--json jobs --jq '.jobs[] | select(.conclusion == "failure") | "\(.name)\t\(.conclusion)"'
Get the failure logs:
gh run view <RUN_ID> --repo mckinsey/agents-at-scale-ark --log-failed 2>&1 | tail -100
Step 3: Categorize Failures
Classify each failure into one of these categories and delegate accordingly:
Category: E2E Test Failures (Chainsaw)
Indicators: E2E Standard, chainsaw in job name, FAIL: chainsaw/ in logs
Skill: Use chainsaw skill to debug and fix failing tests
Common causes:
- Timeout issues in LLM-dependent tests
- CRD schema changes not reflected in test fixtures
- Mock service configuration drift
- Antipattern:
kubectl wait --for=condition=...crashing with nil accessor error — use chainsawassertwith JMESPath instead (see chainsaw skill Antipatterns section) Antipattern check: When a chainsaw test fails with.status.conditions accessor error: <nil> is of the type <nil>, use the chainsaw skill to replace thekubectl waitcommand with a chainsaw assert block.
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.
- 7d ago First seen · 391 lines · 0 tokens per session scan A 1ac37c1bb74a
ark-build-manager is an agent published in the GitHub repository mckinsey/agents-at-scale-ark (422 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 4,117 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
cicd-automation-terraform-specialist
Expert Terraform/OpenTofu specialist mastering advanced IaC automation, state management, and enterprise infrastructure patterns. Handles complex module design, multi-cloud deployments, GitOps workflows, policy as code, and CI/CD integration. Covers migration strategies, security best practices, and modern IaC…
nw-platform-architect-reviewer
Use for review and critique tasks - Platform design, CI/CD pipeline, infrastructure, observability, deployment readiness, and production handoff review specialist. Runs on Haiku for cost efficiency.
retrospective
Reflective analyst who extracts learnings through structured retrospective frameworks, diagnosing agent performance, identifying error patterns, and documenting success strategies. Uses Five Whys, timeline analysis, and learning matrices. Use when you need root-cause analysis, atomicity scoring, or to transform…
09-Diagnose
Interactive diagnostic agent that guides users through Azure resource health assessment, issue identification, and remediation planning. Approval-first execution, single-resource scope, reports to agent-output/{project}/.
debug
Debug your application to find and fix a bug. Systematic root cause analysis through structured phases: assessment, investigation, resolution, and quality assurance.
fixer
Reproduce failures and apply targeted fixes.