athena（athena 搜索）

Athena — Active Knowledge Intelligence

Run the Mine → Grow → Defrag cycle to keep the knowledge flywheel healthy.

What This Skill Does

The flywheel captures learnings reactively (via /retro, /post-mortem). Athena closes the loop by actively mining for unextracted signal, validating existing learnings against current code, synthesizing cross-domain insights, and cleaning up stale or duplicate artifacts.

When to use: Before an evolve cycle, after a burst of development, or weekly. Athena is non-destructive — it proposes changes without modifying existing learnings.

Output: .agents/athena/YYYY-MM-DD-report.md

Execution Steps

Step 1 — Mine: Extract Signal

Run mechanical extraction. Mine scans git history, .agents/research/, and code complexity hotspots for patterns never captured as learnings.

ao mine --since 26h                    # default: all sources, last 26h
ao mine --since 7d --sources git,agents  # wider window, specific sources

Read .agents/mine/latest.json and extract: co-change clusters (files changing together), orphaned research (unreferenced .agents/research/ files), and complexity hotspots (high-CC functions with recent edits).

Fallback (no ao CLI): Use git log --since="7 days ago" --name-only to find recurring file groups. List .agents/research/*.md and check references in learnings.

Assign Initial Confidence. For every new learning candidate extracted, assign a confidence score based on evidence strength:

Also assign a scope tag: project:<name> (project-specific), language:<lang> (language convention), or global (universal pattern). Default to project:<current> unless the pattern is clearly language- or tool-universal.

Write the confidence and scope into the learning frontmatter:

---
title: "Learning title"
confidence: 0.3
scope: project:agentops
observed_in:
  - session: "YYYY-MM-DD"
    context: "Brief description of observation"
---

Step 2 — Grow: LLM-Driven Synthesis

This is the reasoning phase. Perform each sub-step using tool calls.

Flywheel Health Diagnostic: Compute σ (consistency), ρ (velocity), δ (decay) and report escape velocity status. See references/flywheel-diagnostics.md for measurement commands and remediation actions.

2a. Validate Top Learnings and Adjust Confidence

Select the 5 most recent files from .agents/learnings/. For each:

1. Read the learning file (including its confidence and scope frontmatter)
2. If it references a function or file path, use Read to verify the code still exists
3. Classify as: validated (matches), stale (changed), or contradicted (opposite)
4. Adjust confidence based on validation result:

- Validated and still accurate: +0.1 (cap at 0.9) - Stale but partially true: no change (mark for review) - Contradicted by current code: -0.2 (floor at 0.1, flag for removal) - Pattern validated in a new project: +0.15 - Not referenced in 30+ days: -0.05 (time decay) - Not referenced in 90+ days: -0.1 (time decay)

Update the learning file frontmatter with the new confidence score.

Auto-Promotion Rule: After confidence adjustment, check if the learning's confidence is > 0.7. If so, and it is not already in MEMORY.md, promote it:

1. Add the learning's key insight to the relevant MEMORY.md topic file
2. Log: "Promoted '<title>' to MEMORY.md (confidence: <score>)"
3. If the same pattern appears in 2+ projects with confidence >= 0.8, promote its scope from project:<name> to global

2b. Rescue Orphaned Research

For each orphaned research file from mine output: read it, summarize the key insight in 2-3 sentences, and propose as a new learning candidate with title and category.

2c. Cross-Domain Synthesis

Group mine findings by theme (e.g., "testing patterns", "CLI conventions"). For themes with 2+ findings, write a synthesized pattern candidate capturing the common principle.

2d. Gap Identification

Compare mine output topics against existing learnings. Topics with no corresponding learning are knowledge gaps. List each with: topic, evidence, suggested learning title.

Step 3 — Defrag: Mechanical Cleanup

Run cleanup to find stale, duplicate, and oscillating artifacts.

ao defrag --prune --dedup --oscillation-sweep

Read .agents/defrag/latest.json and note: orphaned learnings (unreferenced, >30 days old), near-duplicate pairs (>80% content similarity), and oscillating goals (alternating improved/fail for 3+ cycles).

Fallback: find.agents/learnings -name "*.md" -mtime +30 for stale files. Check .agents/evolve/cycle-history.jsonl for alternating result patterns.

Normalization Defect Scan

During defrag, scan the learnings and patterns pool for structural defects that degrade flywheel quality:

# Placeholder patterns: files with only frontmatter, no content after closing ---
for f in .agents/patterns/**/*.md .agents/learnings/**/*.md; do
  [ -f "$f" ] || continue
  content_after_frontmatter=$(awk '/^---$/{n++; if(n==2) found=1; next} found{print}' "$f" | grep -c '[^ ]')
  [ "$content_after_frontmatter" -eq 0 ] && echo "PLACEHOLDER: $f"
done

# Stacked frontmatter: multiple --- delimiter pairs (>2 occurrences of ^---$)
grep -rl '^---$' .agents/learnings/ .agents/patterns/ 2>/dev/null | while read f; do
  count=$(grep -c '^---$' "$f")
  [ "$count" -gt 2 ] && echo "STACKED_FRONTMATTER: $f ($count delimiters)"
done

# Bundled multi-learning files: more than one ## Learning heading
grep -rl '^## Learning' .agents/learnings/ 2>/dev/null | while read f; do
  count=$(grep -c '^## Learning' "$f")
  [ "$count" -gt 1 ] && echo "BUNDLED: $f ($count learnings in one file)"
done

# Duplicated headings within a file
for f in .agents/learnings/**/*.md .agents/patterns/**/*.md; do
  [ -f "$f" ] || continue
  dupes=$(grep '^## ' "$f" | sort | uniq -d)
  [ -n "$dupes" ] && echo "DUPLICATE_HEADING: $f — $dupes"
done

Report normalization defects in the defrag output. If any are found, list them with severity:

• PLACEHOLDER → HIGH (empty knowledge pollutes retrieval)
• STACKED_FRONTMATTER → MEDIUM (parsing errors, possible data loss)
• BUNDLED → HIGH (breaks per-learning citation tracking)
• DUPLICATE_HEADING → LOW (cosmetic, may confuse extraction)

These defects should be flagged for manual review or automatic splitting during the next forge cycle.

Step 4 — Report

mkdir -p .agents/athena

Write .agents/athena/YYYY-MM-DD-report.md:

# Athena Report — YYYY-MM-DD

## New Learnings Proposed
- [title]: [summary] (source: [research file or synthesis])

## Validations
- Validated: N | Stale: N (list files) | Contradicted: N (list with explanation)

## Knowledge Gaps
- [topic]: [evidence] → suggested learning: "[title]"

## Defrag Summary
- Orphaned: N | Duplicates: N | Oscillating goals: N

## Recommendations
1. [Actionable next step]

If bd is available, create issues for knowledge gaps:

bd add "[Knowledge Gap] <topic>" --label knowledge --label athena

Report findings to the user: proposed learnings, validation results, gaps, and defrag actions recommended.

Scheduling / Auto-Trigger

Lightweight defrag (prune + dedup, no mining) runs automatically at session end via the athena-session-defrag.sh hook. This keeps the knowledge store clean without requiring manual /athena invocations. The hook:

• Fires on every SessionEnd event after session-end-maintenance.sh
• Skips silently if the ao CLI is not available
• Runs only ao defrag --prune --dedup (no --oscillation-sweep or mining)
• Has a 20-second timeout to avoid blocking session teardown

For a full Mine → Grow → Defrag cycle, invoke /athena manually.

Examples

User says: /athena — Full Mine → Grow → Defrag cycle, report in .agents/athena/.

User says: /athena --since 7d — Mines with a wider window (7 days).

Pre-evolve warmup: Run /athena before /evolve for a fresh, validated knowledge base.

Troubleshooting

Reference Documents

• references/confidence-scoring.md
• references/knowledge-synthesis-patterns.md
• references/flywheel-diagnostics.md