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T1KTheOneKit

t1k:watzup

FieldValue
Modulet1k-base
Version2.17.3
Effortlow
Tools—

Keywords: progress, review, session, standup, summary, today, wrap-up

How to invoke

Section titled “How to invoke”
/t1k:watzup

TheOneKit Watzup — Session Review

Section titled “TheOneKit Watzup — Session Review”

Session wrap-up: git history + state checks + task review. No delegation needed — built-in.

Live Context (fetch on demand — do NOT inline in body)

Section titled “Live Context (fetch on demand — do NOT inline in body)”

When the user asks for a session wrap-up, fetch live context via tool calls AFTER the body is loaded — NEVER embed inline shell-substitution tokens (the !-prefix-then-backtick syntax) here (cache-busts on every commit). Use:

  • Bash git log --oneline -10 → recent commits (use Bash; it’s per-call execution, not body content)
  • Read .t1k-module-summary.txt → module summary (file may not exist; treat absence as “no modular kits”)
  • TaskList → in-progress and pending tasks

Skip silently if a source is unavailable — do not echo error.

Process

Section titled “Process”

Run all steps in order:

  1. Git history — git log --oneline -10 — list recent commits with types
  2. State check — read console/logs for errors or warnings; note if clean
  3. Task check — TaskList — list in-progress and pending tasks; flag blockers
  4. Completion gate audit — for any code changes in session, check:
    • Gate 1: Were affected skills updated?
    • Gate 2: Is the codebase compiling/running cleanly?
    • Gate 3: Do new/modified systems have tests?
    • Gate 4: Module integrity — /t1k:doctor module checks pass? (if installedModules present)
  5. Telemetry review (if features.telemetry enabled) — see Telemetry Section below. Also read pending-skill-updates.jsonl and pending-issue-submissions.jsonl from .claude/telemetry/ — count unsubmitted entries (lines where submitted !== true). If count > 0, include in the Telemetry section: Pending pipeline submissions: N lesson updates, M issue submissions.
  6. Summary — output structured status report

Regression Detection

Section titled “Regression Detection”

When telemetry data is available, compare error patterns across sessions:

  1. Read .claude/telemetry/errors-*.jsonl — sort files by name (date order), take last 5
  2. Parse current session errors vs prior sessions:
    • New error type (not seen in last 5 sessions): emit [REGRESSION] {error_type} — not seen in last 5 sessions
    • Error count increase > 50%: emit [SPIKE] {error_type} — {N}x increase vs last session
  3. If no telemetry files exist: skip silently (no output)
  4. Include regression/spike alerts in ### State section of summary output

Telemetry Section

Section titled “Telemetry Section”

If features.telemetry is enabled in any t1k-config-*.json:

  1. Read all .claude/telemetry/*.jsonl files (skip archived/)
  2. Aggregate:
    • Errors: group by error pattern, count occurrences, show top 5
    • Hook errors: read hook-errors-*.jsonl — group by hook name, count failures, show top 5. If any hook has 3+ failures, recommend filing /t1k:issue
    • Hook diagnostics: read hook-log-{today}.jsonl and hook-log-{yesterday}.jsonl (if features.hookLogging enabled):
      • Error count per hook (entries with crash: true)
      • Median durationMs per hook name (timing profile)
      • Dedup hit rate for hook-runner: count dedup=duplicate vs dedup=first entries
      • Surface as sub-section: ### Hook Diagnostics with hook name, avg duration, error count
    • Skill usage: rank by activation count, list never-used skills
    • Feature gaps: list unique queries with zero matching skills
  3. Present in session summary under ### Telemetry section
  4. Offer GitHub submission: ask user “Submit telemetry to kit repo?”
  5. If approved:
    • Determine target repo from t1k-config-*.json → repos.primary
    • Create issue: gh issue create --repo {repo} --title "[telemetry] Session report {date}" --label "telemetry" --body "{aggregated report}"
    • Move processed files to .claude/telemetry/archived/
  6. If declined: leave files for next session aggregation

Output Format

Section titled “Output Format”
## Session Summary — {date}


### Commits
- {hash} {type}: {description}


### State
- [CLEAN | N errors listed]


### Installed Modules (if installedModules present in metadata.json)
- Module: {module-name} v{version} (kit: {kit-name}, preset: {preset if any})
- Modules: {comma-separated installed module names with versions}
- Available: {comma-separated not-installed module names}


### Tasks
- In-progress: {list or "none"}
- Pending: {list or "none"}
- Blockers: {list or "none"}


### Completion Gates
- Skill sync: [PASS/NEEDS UPDATE — which skills]
- Clean state: [PASS/FAIL]
- Test coverage: [PASS/NEEDS TESTS]


### Telemetry (if enabled)
- Errors: {count} patterns ({top error types})
- Skill usage: {count} activations ({top skills})
- Feature gaps: {count} unmatched queries
- Pending pipeline: N lesson updates, M issue submissions (or "none pending")
- Submission: [PENDING/SUBMITTED/DECLINED]


### Hook Diagnostics (if features.hookLogging enabled)
- {hook-name}: avg {N}ms, {N} crashes, dedup rate {N}% (hook-runner only)
- Slowest hook: {name} ({N}ms avg)


### Recommended Next Actions
1. {action}

Future: AI-Aggregated Gotcha Generation

Section titled “Future: AI-Aggregated Gotcha Generation”

The telemetry data collected during /t1k:watzup sessions feeds into the Self-Improving AI Pipeline (see CLAUDE.md). Error patterns aggregated across users will be used by a scheduled AI agent to auto-generate gotcha entries and preventive guidance, distributed to all users via kit releases. This skill’s telemetry section is the data source for that pipeline.

Status: Not yet implemented. Currently telemetry is collected and optionally submitted as GitHub issues. The AI aggregation layer is planned.

Scope

Section titled “Scope”

Session status review only — does NOT implement, modify, or commit.