AgentSkillsCN

status

展示 ALM 学习进度仪表盘,其中包含会话次数、信心分数、分类器状态,以及建议采取的行动。

SKILL.md
--- frontmatter
name: status
description: Show ALM learning progress dashboard with session counts, confidence scores, classifier status, and suggested actions.
disable-model-invocation: true
user-invocable: true

ALM Status Dashboard

Display the current state of ALM's learning progress. Read the following files and present a summary:

Data to Gather

  1. State — Read ~/.claude/alm/state.json:

    • evalCount: total sessions tracked
    • paused: whether ALM is paused
    • installDate: when ALM was installed
    • lastReflection: when reflection last ran
  2. Confidence Scores — Read ~/.claude/alm/confidence.json:

    • For each task type, show: score, autonomyLevel, totalOutcomes, recentCorrections, source (seed vs personalized)
  3. Playbooks — List files in ~/.claude/alm/playbooks/:

    • Count learned (personalized) playbooks vs seed-only types
  4. Classifier — Check if ~/.claude/alm/classifier/model.json exists:

    • If yes: read it and report training_samples, task_types, trained_at
    • If no: report "TF-IDF classifier not yet trained"
  5. Reflect Queue — Read ~/.claude/alm/reflect-queue.json (if exists):

    • Show task types queued for reflection and why
  6. Recent Evaluations — Read the most recent .jsonl file in ~/.claude/alm/evaluations/:

    • Show the last 5 evaluations (taskType, outcome, correctionDetected)
  7. Staleness Check — For each task type in confidence.json, check if its lastReflection is 60+ days ago AND its recent correction rate is rising (recentCorrections/recentOutcomes > correctionRateAtLastReflect). Flag stale playbooks with a warning.

Output Format

Present as a clean dashboard:

code
ALM Status
==========
Sessions tracked: {evalCount} | Installed: {installDate} | Paused: {yes/no}
Last reflection: {lastReflection or "never"}

Confidence Scores:
  {task-type}:  {score}  ({autonomyLevel})  [{totalOutcomes} sessions, {recentCorrections} recent corrections]  {source}
  ...

Playbooks: {N} personalized, {M} seed-only
Classifier: {active with N samples / not yet trained}

{If any stale playbooks detected:}
Stale Playbooks:
  {task-type} playbook may be stale (last updated {N} days ago, correction rate rising). Consider running /alm:reflect.

{If reflect queue is non-empty:}
Ready for reflection: {types and reasons}
  -> Run /alm:reflect to update

Recent Sessions:
  {date} | {taskType} | {outcome} | corrections: {yes/no}
  ...

If ALM has no data yet (fresh install), show a brief welcome message explaining what to expect.