reflect

Reflect — Human AI-Usage Self-Assessment

This skill is for you (the human) to reflect on how well you're leveraging AI as a thought amplifier — not a review of agent performance.

Data source: The reflect hook (session-end) automatically collects observations, analyzes patterns, and updates metrics.json. This skill consumes that hook-produced data to produce a human-readable self-assessment.

Hook (auto)                    Skill (on-demand /reflect)
─────────────                  ──────────────────────────
observe → obs/*.jsonl  ──→     epic reflect --context 30
evolve → metrics.json  ──→     5-dimension scorecard
seed → evolved skills  ──→     Action items for the human
ingest → memory graph  ──→     Trend analysis

Iron Law

No score without evidence. Every rating must directly cite at least one of: obs stats, evolution patterns, memory nodes, or session summaries. Block self-serving bias: "doing well" conclusions require concrete metrics.

Process

Step 0 — Collect Context

# Uses Rust subcommand — works on all platforms (Linux, macOS, Windows)
epic reflect --context 30 > /tmp/reflect_ctx.json

Fallback if subcommand fails:

echo "obs_files: $(ls "$HARNESS_DIR/obs/" | wc -l)"
python3 -c "import json; m=json.load(open('$HARNESS_DIR/metrics.json')); print('total_sessions:', m.get('total_sessions',0))"

Query memory (if active):

epic mem recall "AI usage patterns decisions metacognition" --limit 8
epic mem list --type decision --limit 5
epic mem list --type pattern --limit 5

Step 1 — 5-Dimension Reflection

Score each dimension independently: 1–10 + evidence citation + one-line diagnosis.

Dimension 1: Thought Amplification

Question: Is AI a mere executor (code typist) or a genuine thought partner?

Metrics:

• Agent tool call ratio (Agent / total_obs — higher = delegated thinking)
• Skill invocation frequency (meta-layer usage)
• council/discover/spec execution history
• Memory decision node count