Yohayetsion Product Org Os Interaction Recall

Query past interactions — agent conversations, gateway sessions, and skill invocations — to understand what was discussed, who contributed, and what conclusions were reached.

Vision to Value Phase

Cross-phase — This skill surfaces conversation history relevant to any phase of work.

Prerequisites: Interaction log populated (interactions logged via the interaction logging system) Outputs used by: Any agent or skill needing prior conversation context

Purpose

Before starting work in an area that may have been discussed before, query the interaction log to surface:

• What was previously discussed on this topic
• Which agents contributed perspectives
• What conclusions or outcomes were reached
• What open questions remain

This complements /context-recall (which searches decisions, bets, and learnings) by searching conversation history — the request/response pairs that led to those decisions.

When to Use

Invoke /interaction-recall [topic] when:

• Starting a new session and want to pick up where you left off
• Checking what agents previously said about a topic
• Auditing how a conclusion was reached
• Understanding the progression of thinking on a topic
• Finding which agents have weighed in on an area

Query Syntax

/interaction-recall pricing                      → All pricing interactions
/interaction-recall pricing agent:pm             → Only PM's pricing interactions
/interaction-recall pricing date:2026-01         → January 2026 only
/interaction-recall pricing product:AXIA         → Product-filtered
/interaction-recall pricing type:gateway         → Only gateway sessions
/interaction-recall agent:pm                     → All PM interactions (any topic)
/interaction-recall date:2026-01-27              → All interactions on a specific date

Filters

Multiple filters can be combined: /interaction-recall pricing agent:pm product:AXIA

Process

1. Parse Query

Extract:

• Topic keywords: The main search terms
• Filters: agent, date, product, type (optional)

2. Search JSON Index

Read context/interactions/index.json and search:

1. Topic search: Match keywords against topicIndex keys and entry topics arrays
2. Request/outcome search: Match keywords against requestSummary and outcomeSummary fields
3. Apply filters: Filter results by agent, date, product, type as specified

3. Rank Results

Order by relevance:

1. Exact topic match in topicIndex
2. Keyword match in requestSummary or outcomeSummary
3. Partial topic match
4. Most recent first (within same relevance tier)

4. Retrieve Full Content

For the top 5 matches:

• Read the daily log file at entries[].path
• Find the specific entry by ID within the file
• Extract full request and response content

5. Present Results

## Interaction History: [Topic]

*Found [N] interactions matching "[query]"*
*Filters: [applied filters]*

---

### IX-2026-00042 | {emoji} @pm | 2026-01-27 14:23
**Topics**: prd, authentication, requirements-gaps
**Product**: AXIA

**Request**: Review authentication PRD for requirements gaps

**Outcome**: Found 3 gaps: SSO edge cases, session timeout, MFA fallback

**Related**: DR-2026-003, DOC-2026-015

<details>
<summary>Full conversation</summary>

[Full request and response text]

</details>

---

### IX-2026-00038 | {emoji} @vp-product | 2026-01-25 10:15
...

---

### Related Context

These skills may surface additional information:
- `/context-recall [topic]` — Decisions, bets, learnings on this topic
- `/feedback-recall [topic]` — Customer feedback on this topic

*Showing top 5 of [N] matches. Narrow with filters: `agent:`, `date:`, `product:`, `type:`*

6. Handle No Results

If no interactions match:

## Interaction History: [Topic]

No interactions found matching "[query]".

**Suggestions**:
- Try broader keywords
- Remove filters to widen search
- Check `/context-recall [topic]` for decisions and documents
- Check `/feedback-recall [topic]` for customer feedback

Instructions

1. Accept topic/keyword from user (at least one keyword or filter required)
2. Parse optional filters: agent:, date:, product:, type:
3. Read context/interactions/index.json
4. Search entries using topic matching + filters
5. Rank results by relevance, then recency
6. For top 5 matches, read full content from daily log files
7. Present results with summaries and expandable full content
8. Suggest related context skills (/context-recall, /feedback-recall)
9. If no matches, provide helpful suggestions

Example Usage

User: /interaction-recall pricing

Agent: Searches context/interactions/index.json for topic "pricing"
       Returns matching interactions with summaries and full content
       Suggests /context-recall pricing for related decisions

User: /interaction-recall agent:pm date:2026-01

Agent: Returns all PM interactions from January 2026
       Ordered by date, most recent first

User: /interaction-recall enterprise product:AXIA type:gateway

Agent: Returns gateway sessions about enterprise topics for AXIA
       Shows all agent perspectives from those sessions

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