Inject Skill

Typically runs automatically via SessionStart hook.

Inject relevant prior knowledge into the current session.

How It Works

The SessionStart hook runs:

ao inject --apply-decay --format markdown --max-tokens 1000

This searches for relevant knowledge and injects it into context.

Manual Execution

Given /inject [topic]:

Step 1: Search for Relevant Knowledge

With ao CLI:

ao inject --context "<topic>" --format markdown --max-tokens 1000

Without ao CLI, search manually:

# Recent learnings
ls -lt .agents/learnings/ | head -5

# Recent patterns
ls -lt .agents/patterns/ | head -5

# Recent research
ls -lt .agents/research/ | head -5

# Global patterns (cross-repo knowledge)
ls -lt ~/.claude/patterns/ 2>/dev/null | head -5

Step 2: Read Relevant Files

Use the Read tool to load the most relevant artifacts based on topic.

Step 3: Summarize for Context

Present the injected knowledge:

• Key learnings relevant to current work
• Patterns that may apply
• Recent research on related topics

Step 4: Record Citations (Feedback Loop)

After presenting injected knowledge, record which files were injected for the feedback loop:

mkdir -p .agents/ao
# Record each injected learning file as a citation
for injected_file in <list of files that were read and presented>; do
  echo "{\"learning_file\": \"$injected_file\", \"timestamp\": \"$(date -Iseconds)\", \"session\": \"$(date +%Y-%m-%d)\"}" >> .agents/ao/citations.jsonl
done

Citation tracking enables the feedback loop: learnings that are frequently cited get confidence boosts during /post-mortem, while uncited learnings decay faster.

Knowledge Sources

Source	Location	Priority
Learnings	.agents/learnings/	High
Patterns	.agents/patterns/	High
Research	.agents/research/	Medium
Retros	.agents/retros/	Medium
Global Patterns	~/.claude/patterns/	High

Decay Model

Knowledge relevance decays over time (~17%/week). More recent learnings are weighted higher.

Key Rules

• Runs automatically - usually via hook
• Context-aware - filters by current directory/topic
• Token-budgeted - respects max-tokens limit
• Recency-weighted - newer knowledge prioritized

Examples

SessionStart Hook Invocation

Hook triggers: session-start.sh runs at session start

What happens:

1. Hook calls ao inject --apply-decay --format markdown --max-tokens 1000
2. CLI searches .agents/learnings/, .agents/patterns/, .agents/research/ for relevant artifacts
3. CLI applies recency-weighted decay (~17%/week) to rank results
4. CLI outputs top-ranked knowledge as markdown within token budget
5. Agent presents injected knowledge in session context

Result: Prior learnings, patterns, research automatically available at session start without manual lookup.

Manual Context Injection

User says: /inject authentication or "recall knowledge about auth"

What happens:

1. Agent calls ao inject --context "authentication" --format markdown --max-tokens 1000
2. CLI filters artifacts by topic relevance
3. Agent reads top-ranked learnings and patterns
4. Agent summarizes injected knowledge for current work
5. Agent references artifact paths for deeper exploration

Result: Topic-specific knowledge retrieved and summarized, enabling faster context loading than full artifact reads.

Troubleshooting

Problem	Cause	Solution
No knowledge injected	Empty knowledge pools or ao CLI unavailable	Run /post-mortem to seed pools; verify ao CLI installed
Irrelevant knowledge	Topic mismatch or stale artifacts dominate	Use --context "<topic>" to filter; prune stale artifacts
Token budget exceeded	Too many high-relevance artifacts	Reduce --max-tokens or increase topic specificity
Decay too aggressive	Recent learnings not prioritized	Check artifact modification times; verify --apply-decay flag