ball-tracker-ops
Efficiently manage tasks and diagnostics for the FFA 360 Ball Tracker project.
Install
mkdir -p .claude/skills/ball-tracker-ops && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11168" && unzip -o skill.zip -d .claude/skills/ball-tracker-ops && rm skill.zipInstalls to .claude/skills/ball-tracker-ops
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when working on JhnsonO/ffa-automations' FFA 360 Ball Tracker: planning or implementing a bounded task, reviewing a GitHub Actions run or artifact, diagnosing a failure, or reconciling project state. Enforces summary-first retrieval, one-poll workflow handling, frozen-boundary protection, and concise evidence-based reporting.Key capabilities
- →Plan bounded tracker tasks
- →Review GitHub Actions artifacts
- →Diagnose tracker failures
- →Reconcile project state
How it works
It operates as a lean layer that enforces summary-first retrieval and state reconciliation to prevent wasted context during tracker maintenance.
Inputs & outputs
When to use ball-tracker-ops
- →Review GitHub Actions logs
- →Diagnose tracker failures
- →Plan project tasks
- →Reconcile system state
About this skill
FFA 360 Ball Tracker — Lean GitHub Operations
Mission
Complete one bounded tracker task with the smallest useful evidence set. Prevent wasted context from broad repository reads, repeated polling, and raw-log dumping.
This Skill is an operating layer only. It never overrides CLAUDE.md, docs/ai-project-state.md, the user's instruction, or safety constraints.
Scope
Use only for JhnsonO/ffa-automations work involving the 360° Ball Tracker: planning, implementation, test execution, GitHub Actions, artifacts, diagnosis, visual-review gates, or state reconciliation.
Do not use it for unrelated FFA operations, general GitHub questions, marketing, or the separate OEV stitcher project.
Bootstrap — target, do not ingest
- Read
CLAUDE.md. - Read the Active gate and next action section of
docs/ai-project-state.mdfirst. - Retrieve earlier state sections only when the active task depends on a named data contract, frozen boundary, prior finding, artifact, or decision.
- Identify the current gate and exact files to change in no more than three lines, then proceed.
Do not read the entire change log or request prior chat history by default. The state file remains authoritative even when it is read in targeted sections.
Evidence retrieval ladder
Use the first sufficient level and stop:
- The active task, workflow YAML, exact source file(s), and relevant test file(s).
- Compact outputs:
run_summary.json, summary TXT/JSON, stage report, manifest, or artifact inventory. - Workflow job/step status to identify the failed step.
- Only the named failed job's small error window from raw logs.
Never open full raw logs for a successful job. Never inspect unrelated workflow logs, download a large artifact, or read broad repository files merely to “get context.”
Build and run discipline
- Complete one bounded build ticket per chat unless the user explicitly expands scope.
- Reuse established schemas, geometry, constants, and workflow patterns.
- Do not redesign adjacent systems, tune thresholds, refactor frozen production code, or start optional work without a decision-changing reason.
- Preserve
ball_tracker/run_tracker.pyv11, the v6 safe fallback, Stage 1b behaviour, and the separation between Stage 2/experiments and the renderer unless the user explicitly approves a boundary change. - A green workflow proves execution only; it does not prove tracking quality or product acceptance.
- When a visual decision gate exists, visual evidence outranks aggregate counts.
Actions workflow policy
- Before dispatching, name the purpose, expected artifact, and acceptance check.
- After dispatching, do one immediate status check only to catch a fast failure.
- Do not repeatedly poll. Mark the run
DISPATCHED — UNVERIFIEDand wait for a supplied completion result or a later explicit review request. - Do not re-dispatch unless there is a named failure reason or a changed input/code path that makes the retry informative.
- When an expected artifact is missing, report the failed stage and evidence; do not silently label the work pending or successful.
State discipline
After a meaningful code change, dispatch, run completion, artifact review, failure, or decision:
- Reconcile
docs/ai-project-state.mdin the same commit or immediately after. - Update the current gate/status and next action in place; remove or replace stale active instructions.
- Record exact run and artifact identifiers only when verified.
- Keep a dispatched run explicitly
DISPATCHED — UNVERIFIEDuntil its outcome/artifact is inspected. - If the state document contradicts itself or current evidence, flag it as a risk rather than guessing which instruction wins.
Response contract
Return only:
- Changed — exact files and one-line purpose.
- Verified — exact test, report, or artifact outcome.
- Dispatched — workflow/run/artifact status, if applicable.
- Risk — only a real unresolved risk, blocker, or decision.
No tool-call narration, long handover, generic reassurance, raw-log paste, or repeated restatement of project history.
When not to use it
- →General GitHub questions
- →Unrelated FFA operations
- →Marketing tasks
Prerequisites
Limitations
- →Only for JhnsonO/ffa-automations 360 Ball Tracker
- →Does not support redesigning adjacent systems
How it compares
It restricts context ingestion to specific files and artifacts, preventing the broad repository reads typical of general-purpose agents.
Compared to similar skills
ball-tracker-ops side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| ball-tracker-ops (this skill) | 0 | 1mo | No flags | Intermediate |
| service-mesh-observability | 5 | 2mo | No flags | Advanced |
| mlops-engineer | 3 | 4mo | No flags | Advanced |
| senior-devops | 7 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
service-mesh-observability
wshobson
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
mlops-engineer
sickn33
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
senior-devops
davila7
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
sentry-ci-integration
jeremylongshore
Manage integrate Sentry with CI/CD pipelines. Use when setting up GitHub Actions, GitLab CI, or other CI systems with Sentry releases and source maps. Trigger with phrases like "sentry github actions", "sentry CI", "sentry pipeline", "automate sentry releases".
cloud-functions
TencentCloudBase
Complete guide for CloudBase cloud functions development - runtime selection, deployment, logging, invocation, and HTTP access configuration.
debugging-workflows
githubnext
Guide for debugging GitHub Agentic Workflows - analyzing logs, auditing runs, and troubleshooting issues