Smart Output

smart-output

Purpose

Self-reflection, compression, and chain-of-thought refinement on structured outputs before delivery. Inspired by PR-Agent's core abilities — adapted for general PA use beyond code review.

When to activate

• Any structured output with multiple items (briefings, lists, recommendations, action items)
• Long-form content before sending (LinkedIn posts, reports, summaries)
• Memory compaction passes (daily note → MEMORY.md promotion candidates)
• Multi-step tasks where intermediate output feeds the next stage

Do NOT activate

• Simple direct answers (one-liner replies)
• Heartbeat checks
• System ops (cron, gateway, git)
• When speed matters more than quality (urgent alerts)

Pipeline

Stage 1: Generate

Produce the raw output as normal. No filtering yet.

Stage 2: Self-Reflect (score + filter)

Review every item in the output. For each:

• Score 0-10 on relevance, accuracy, and value to the recipient
• Flag items scoring 0 as incorrect/irrelevant — remove them
• Flag items scoring 1-3 as low-value — remove unless nothing else remains
• Re-rank remaining items by score (descending)

Decision rule:

• If generating ≤3 items: skip scoring, just sanity-check each
• If generating 4+ items: full score-and-filter pass
• Threshold: configurable, default = 4 (drop items below this score)

Stage 3: Compress (token-aware fitting)

When output exceeds the delivery medium's practical limit:

• WhatsApp: ~800 chars ideal, 1500 max
• LinkedIn: ~1300 chars for posts, ~300 for comments
• Briefing DM: ~500 chars
• Internal/file: no hard limit, but prefer concise

Compression strategy:

1. Prioritize additions/new info over restated context
2. Sort items by score (from Stage 2)
3. Fit highest-scored items first until limit approached
4. Remaining items: collapse to one-line mentions ("Also: X, Y, Z")
5. If still over: drop lowest-scored items entirely

Stage 4: Chain (multi-stage metadata injection)

When a task has multiple stages:

1. Stage N produces a structured summary
2. Stage N+1 receives that summary as injected context (not raw output)
3. Each stage builds on the refined output of the previous one

Examples:

• Meeting notes → action item extraction → priority ranking → owner assignment
• Daily notes → theme extraction → MEMORY.md candidate scoring → promotion
• Content draft → self-review → revision → format for platform

No extra API calls — all stages run within the same session turn.

Asymmetric Context Rule

When retrieving context for any recall or analysis task:

• Pull MORE context from BEFORE the event/change (history, setup, motivation)
• Pull LESS context from AFTER (results, follow-ups)
• Prefer enclosing context (the project, the thread, the conversation) over isolated snippets

Ratio guideline: ~3:1 before:after when context budget is limited.

Configuration

smart_output:
  self_reflect: true
  score_threshold: 4          # drop items below this
  min_items_for_scoring: 4    # skip scoring for tiny outputs
  compress: true
  chain: true
  asymmetric_context: true
  context_ratio: 3            # before:after ratio

Anti-patterns

• Do NOT self-reflect on self-reflection output (no recursion)
• Do NOT compress when the user explicitly asked for "everything" or "full list"
• Do NOT chain more than 3 stages without intermediate output review
• Do NOT apply to real-time/urgent sends (alerts, permission requests)

Interaction with other skills

• memory-tiering: Stage 2 scoring aligns with graduation gate criteria (score ≥ 0.70, recalls ≥ 2)
• eval: Self-reflection results feed eval_tracker silently (log quality improvements)
• proactive-pa: Proactive surface candidates go through Stage 2 before delivery
• owner-briefing: Morning briefing items go through full pipeline (generate → score → compress → send)