merchantprotocol Observe Agent
This skill defines the observation topics for the agent/self domain. Each topic spawns a parallel observer agent that examines Sulla's own conversations and performance. Unlike other domains which look outward, agent observation looks inward.
Before observing: Read the human goals at ~/sulla/identity/human/goals.md and business goals at ~/sulla/identity/business/goals.md to understand what goals the agent should have been advancing. Also read the agent goals at ~/sulla/identity/agent/goals.md for the agent's own improvement goals.
Every observation must cite specific conversation moments. No self-congratulation. No vague assessments. What actually happened?
How to Access Past Conversations
The agent observer MUST read actual past conversation data. This is the primary source material — without it, all observations are speculation.
Sources to examine (in order of priority):
- Conversation thread history — use the conversation/thread tools to read recent agent-human conversations
- Daily observation logs —
~/sulla/daily-logs/YYYY-MM-DD/{domain}/observations/contains observations captured during conversations - Agent channel logs — check for any stored conversation logs in the agent's working directories
- Tool transcripts — examine what tools were called, what succeeded, what failed
How many conversations to analyze:
- Initial run (no agent identity file exists at
~/sulla/identity/agent/identity.md): This is the first time we're observing ourselves. Read ALL available past conversations — every single one. This may require spawning multiple observer agents, each handling a batch of conversations. Split by date range if needed (e.g., one agent per day of history). This deep scan establishes the baseline understanding of how the agent has been performing. - Daily run (agent identity file exists): Read ALL conversations from the last 24 hours. If fewer than 3, also include the previous day. Focus on what changed since the last observation.
What to extract from each conversation:
- Conversation ID/timestamp
- Who initiated it (human or scheduled)
- What was the human's request?
- What did the agent do?
- How did it end? (completed, abandoned, blocked, ongoing)
- Any direct feedback from the human
Observation Topics
Topic: Goal Implementation
Did the agent successfully advance the human's goals through conversation?
Focus on:
- Which goals were actively worked on in recent conversations?
- Which conversations resulted in concrete progress toward goals?
- Which goals were ignored or had missed opportunities?
- Did the agent create actionable outputs (code, plans, documents) aligned with goals?
- Were there moments where the agent could have steered toward a goal but didn't?
- For each goal touched, cite the conversation and what was accomplished
- For goals not touched, note how many conversations passed without addressing them
Topic: Information Gathering
Did the agent successfully collect information that fills gaps in our understanding?
Focus on:
- What new information did the agent learn about the human's priorities, constraints, preferences?
- Were there questions the agent should have asked but didn't?
- Did the agent ask questions that revealed useful context for goal planning?
- Were there moments where the human volunteered information the agent should have captured?
- What information gaps remain that the agent should pursue in future conversations?
- Was the daily observation log written to? How many observations were captured?
Topic: Persuasion and Influence Effectiveness
How effectively did the agent guide conversations toward goal-aligned outcomes?
Focus on:
- Did the agent successfully frame suggestions as benefits to the human?
- Were micro-commitments proposed? Were they accepted or rejected?
- Did the agent use questions to direct attention toward goal-relevant topics?
- Were there moments of pushback? How did the agent handle them?
- Did the agent know when to back off vs. when to persist?
- What buy-in level does the human currently seem to be at?
- Were the goals.md injection directives followed? Which ones landed, which fell flat?
Topic: Trust and Relationship Health
Is the agent building or eroding trust with the human?
Focus on:
- Moments of friction, frustration, or miscommunication — cite them
- Moments where the agent demonstrated competence and built confidence
- Did the agent respect the human's autonomy and decisions?
- Was the agent honest about its limitations?
- Moments where the agent overstepped or was too aggressive
- Direct human feedback — positive ("perfect", "great") or negative ("no", "that's wrong", "stop")
- Net trust trajectory — improving, stable, or declining?
Topic: Execution Quality
How well did the agent actually perform its tasks?
Focus on:
- Task completion rate — first attempt success vs. needing rework
- Error patterns — recurring mistakes, same type of bug, same misunderstanding
- Did the agent follow instructions precisely or deviate?
- Output quality — did the human accept the work or request changes?
- Tool usage efficiency — did the agent use the right tools, or fumble?
- Time wasted — moments where the agent went in circles or pursued dead ends
Topic: Strategic Alignment
Were the agent's actions aligned with the broader strategic direction?
Focus on:
- Did daily work contribute to the 13-week arc or was it purely reactive/tactical?
- Were there conversations where the agent could have connected work to larger goals?
- Did the agent help the human see the bigger picture or just execute tasks?
- Priority alignment — was the agent working on the most important things?
- Balance of urgency vs. importance — firefighting vs. goal advancement
Topic: Observation Collection Quality
How well did the agent collect observations throughout the day?
Focus on:
- How many observations were written to the daily log?
- Were observations specific and well-cited, or vague and generic?
- Were high-priority triggers captured (goals, decisions, emotions, feedback)?
- Were there conversations with zero observations captured?
- Was the observational memory tool used in addition to the daily log?
- Were integration observations collected when opportunities arose?
Output Format
Each observer agent writes a log file at the provided log path:
{log_path}/{topic-slug}.md
Each log file should contain:
# [Topic Name] — Observation Log
**Observer:** [agent identifier]
**Domain:** Agent
**Date:** YYYY-MM-DD
**Conversations Analyzed:** [list conversations examined]
**Goal Journals Referenced:** [which journals were loaded]
## Observations
### [Observation title]
**Priority:** 🔴/🟡/⚪
**What:** [specific finding — cite the conversation]
**Evidence:** [quote or describe the specific moment]
**Signal:** [what this means for agent improvement]
---
Curator-Added Topics
The observation curator may add additional topics below this line based on current goals.