codex-goal-mining

Codex Goal Mining

Purpose

Recover structured Codex goal history and convert free-form runs into evidence-backed, repeatable suites.

When to use

• The user asks what /goal commands ran locally or across the fleet.
• Large QA, beta, release, localization, model, cleanup, or PR campaigns need retesting.
• The user wants active, paused, blocked, or long-running goals ranked.
• Repeated semantic runs need stable suite names and copy-paste prompts.
• Goal time, thread span, tokens, dates, machines, or reachability matter.

Workflow

1. Collect structured data with scripts/codex-goal-report.py.
   • Fleet report: scripts/codex-goal-report.py --policy ~/.config/codex-goal-mining/fleet-policy.json --json --output ~/.codex/reports/codex-fleet-goals.json
   • Local only: scripts/codex-goal-report.py --local --json
   • Recent window: add --since YYYY-MM-DD.
   • Exact activity window: add --activity-overlap --since <ISO> --until <ISO>. Existing --since semantics remain creation-time based without that flag.
   • Bounded repeat snapshots: add --cursor-file <path> to compute reset-safe deltas. No cursor or report directory is created by default.
   • Selected machines: repeat --machine <fleet-alias>.
   • Require fleet coverage with --fleet; any unreachable source is rendered and returns nonzero instead of silently reporting success.
   • Use --one-attempt for a single configured/first interpreter attempt per host. Policy entries may declare python and wsl_python.
   • Start fleet configuration from references/fleet-policy.example.json; never commit a real private inventory.
2. Treat the data sources correctly.
   • Prefer goals_1.sqlite for objective, status, tokens, and Codex-recorded active goal time.
   • Join state_5.sqlite for thread timestamps and rollout paths.
   • Use JSONL only as a fallback for older installations.
   • Wall-clock thread span includes idle and resume gaps; never describe it as active labor.
   • Goal database counters are lifetime snapshots. Only cursor deltas have an observation interval, and a reset is unknown rather than zero.
   • Preserve root/child identity where the state database supplies it. JSONL fallback reports unknown rather than guessing.
3. Report collection coverage first.
   • List reached and unreachable machines.
   • Give the date range, goal count, statuses, total active goal time, and median goal time.
   • Preserve exact transport blockers instead of silently shrinking the fleet.
4. Mine patterns semantically.
   • Exact duplicate text is weak evidence because operators rephrase goals.
   • Cluster by intended test surface, matrix, exit criteria, and repeated operating contract.
   • Prioritize unfinished goals and recurring high-time campaigns.
   • Separate product beta suites from operational queues such as contributor PR sweeps.
5. Produce reusable reruns.
   • Use [suite:<name>] [baseline:<sha-or-date>] [matrix:<targets>] [exit:<criteria>].
   • Include a fixed matrix, evidence requirements, blocker rules, and definition of done.
   • Start from references/goal-suite-patterns.md, then adapt to current evidence.
6. Keep reports private.
   • Default to terminal/JSON delivery. Use --output only when retained output was requested or required.
   • Scrub secrets, private hosts, personal absolute paths, and credentials before creating a gist or sharing externally.
   • Use a secret gist unless the user explicitly requests public visibility.
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.

Inputs

• Local Codex stores under ~/.codex/.
• Optional fleet policy supplied with --policy or CODEX_FLEET_POLICY.
• Optional date window, machine aliases, suite focus, or output path.

Outputs

• Markdown or JSON fleet goal report.
• Ranked active, paused, blocked, and long-running goals.
• Semantic campaign summary with timing and reachability caveats.
• Stable copy-paste /goal suite prompts and rerun priority order.