agent-device-lab
Prepare and check trusted macOS hosts for physical Android and iPhone tests with agent-device, ADB, and Xcode, or set up iPhone…
Mine structured Codex /goal history locally or across a configured machine fleet, measure active goal time and resumed thread spans, identify unfinished and recurring semantic runs, and turn them into stable copy-paste rerun suites. Use when the user asks to inspect goal
$ npx -y skills add vincentkoc/dotskills --skill codex-goal-mining --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/codex-goal-miningContext preview
The summary Claude sees to decide when to auto-load this skill.
Mine structured Codex /goal history locally or across a configured machine fleet, measure active goal time and resumed thread spans, identify unfinished and recurring semantic runs, and turn them into stable copy-paste rerun suites. Use when the user asks to inspect goal
name: codex-goal-mining description: Mine structured Codex /goal history locally or across a configured machine fleet, measure active goal time and resumed thread spans, identify unfinished and recurring semantic runs, and turn them into stable copy-paste rerun suites. Use when the user asks to inspect goal history, summarize goal commands, find repeated goals, recover large beta campaigns, compare goal duration or token usage, or prepare reusable /goal prompts and privacy-scrubbed reports. license: MIT metadata: workflow-exemption: "Ordered collection, coverage accounting, analysis, and reporting; no separate action-routing lifecycle." source: "https://github.com/vincentkoc/dotskills"
Recover structured Codex goal history and convert free-form runs into evidence-backed, repeatable suites.
1. Collect structured data with `scripts/codex-goal-report.py`.
Existing `--since` semantics remain creation-time based without that flag.
deltas. No cursor or report directory is created by default.
and returns nonzero instead of silently reporting success.
host. Policy entries may declare `python` and `wsl_python`.
2. Treat the data sources correctly.
observation interval, and a reset is unknown rather than zero.
fallback reports unknown rather than guessing. 3. Report collection coverage first.
4. Mine patterns semantically.
5. Produce reusable reruns.
6. Keep reports private.
was requested or required.
7. For retained output or resumable collection state, apply the task artifact contract from `$operations-worktree`. Reuse identity-matched canonical inventories instead of copying large payloads.
We are moving from "prompt as text" to skill as runtime module. Each skill acts like a lightweight, containerized application for AI work: a stable interface, opinionated workflow, and bundled resources that can be installed, versioned, tested, and reused
Prepare and check trusted macOS hosts for physical Android and iPhone tests with agent-device, ADB, and Xcode, or set up iPhone…
Resolve canonical Git checkouts, index and verify codebase-memory-mcp graphs through the guarded CLI, open its browser viewer on…
Recover Codex or Claude sessions from tmux panes, saved cockpit snapshots, session history, and recent agent logs without…
Interpret Crabpot and OpenClaw performance dashboard metrics, import-loop profiles, runtime profiles, and branch-to-branch perf…
Maintain and release the crawlkit Go library, preserving downstream compatibility for gitcrawl, slacrawl, discrawl, and notcrawl.
Template skill for repository authors; excluded from public publishing.