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.

Step 2 — Grow: LLM-Driven Synthesis

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

2a. Validate Top Learnings

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

1. Read the learning file
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)

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.

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.

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

Local Resources

scripts/

• scripts/validate.sh