# Self Assessment — 2026-03-22 10:01 UTC

Overall score: 0.54

```json
{
  "dimensions": [
    {
      "dimension": "reflection_quality",
      "score": 0.05,
      "evidence": "Critical loop failure: 200 observations were created this week (confirmed by learning_velocity and blind_spots topic distribution) but retrieved_count=0 and influenced_count=0. Observations are being written but never read back into decision-making. The reflection pipeline is a write-only system this week \u2014 output with no feedback loop. This is the most severe finding in this assessment.",
      "data_available": true
    },
    {
      "dimension": "procedure_effectiveness",
      "score": 1.0,
      "evidence": "7 active procedures, avg_success_rate=1.0 (100%), zero low performers. No procedures at risk of quarantine. This is the strongest dimension this week \u2014 every learned procedure is working perfectly. Caveat: with 100% success across all 7, worth verifying these are being exercised with meaningful frequency and not just trivially triggered.",
      "data_available": true
    },
    {
      "dimension": "outreach_calibration",
      "score": 0.18,
      "evidence": "Engagement rate: 2 acknowledged / 17 total = 11.8%. Ignored rate: 88.2%. Of what was engaged, 100% was useful (2/2) \u2014 quality of content is fine, but volume/targeting calibration is badly off. By category: alert (1 ack), digest (1 ack, 1 useful), surplus (1 useful). The surplus category produced utility but no acknowledgment event logged. Sending 15 messages that get ignored suggests either frequency too high, timing wrong, or category mis-targeting. Prediction error is structural, not random.",
      "data_available": true
    },
    {
      "dimension": "learning_velocity",
      "score": 0.78,
      "evidence": "200 observations created this week vs 0 last week \u2014 technically infinite acceleration, but last_week=0 almost certainly means this is baseline week 1 rather than a true jump. No procedure creation delta available for comparison. Raw creation rate is strong: 200 observations across 12 topic categories in a single week. Deducting for the caveat that this may be a cold-start artifact and no procedure velocity data is available.",
      "data_available": true
    },
    {
      "dimension": "resource_efficiency",
      "score": 0.32,
      "evidence": "Surplus promotion rate: 3 promoted / 8 reviewed (37.5%) \u2014 below the expected threshold for healthy staging throughput. More concerning: 80 items pending at 8 reviews/week = ~10-week clearance backlog. The queue is growing faster than it's being drained. No cost budget utilization data provided (daily/weekly/monthly percentages absent). No idle compute utilization data provided. Score penalized for backlog severity and missing cost/idle metrics.",
      "data_available": true
    },
    {
      "dimension": "blind_spots",
      "score": 0.28,
      "evidence": "Severe topic concentration: user_model_delta accounts for 124/200 observations (62%). Light_reflection adds another 34 (17%). Combined, two categories own 79% of all observations. Zero observations this week in any drive-related category (preservation, curiosity, cooperation, competence). Zero observations on strategic planning, technical state, or system health. Competitive intelligence: 2 obs (1%). Project context: 3 obs (1.5%). The observation engine is overwhelmingly focused on modeling the user, at the cost of monitoring Genesis's own operational state and broader environment.",
      "data_available": true
    }
  ],
  "overall_score": 0.54,
  "observations": [
    "The reflection loop is broken as a feedback mechanism: 200 observations written, 0 retrieved. The memory system is accumulating data that influences nothing. This is the highest-priority finding.",
    "Outreach volume calibration is badly miscalibrated: 88% ignore rate suggests either over-sending, wrong timing, or wrong category selection \u2014 the 100% utility rate on what was acknowledged rules out content quality as the problem.",
    "Topic concentration in user_model_delta (62%) is creating observational monoculture. Genesis is essentially running blind on its own drives and operational health.",
    "The surplus backlog (80 pending, 8/week throughput) is a slow-motion queue overflow. At current rates, items queued today won't be reviewed for 10 weeks \u2014 a staleness problem compounding over time.",
    "Procedure effectiveness is the one genuine bright spot: 7/7 at 100% success. This infrastructure is sound.",
    "Data inconsistency: reflection_quality.total_observations=0 conflicts with learning_velocity.observations_this_week=200. These fields likely measure different things (retrieved vs. created), but the schema should disambiguate this explicitly."
  ],
  "recommendations": [
    "URGENT \u2014 Fix the retrieval loop: The observation retrieval mechanism is not firing (retrieved_count=0). Diagnose whether this is a query failure, a routing gap, or a missing call site. Until observations influence actions, the entire reflection pipeline is observability theater.",
    "Reduce outreach frequency by at least 50% and A/B test send timing. The 88% ignore rate is not a content problem \u2014 it's a targeting/cadence problem. Consider hold-until-threshold logic: only send outreach when predicted engagement exceeds a minimum confidence.",
    "Introduce drive-activity observation types and add them to the reflection prompts. The absence of preservation/curiosity/cooperation/competence observations means Genesis has no self-model of its motivational state this week.",
    "Address the surplus backlog with either increased review capacity or a staleness discard policy: items older than N days without promotion should be auto-discarded rather than held indefinitely.",
    "Establish minimum weekly observation quotas per category (e.g., \u22655 observations in competitive_intelligence, \u22655 in system health, \u22653 per active drive). This creates anti-concentration pressure without overriding natural signal."
  ]
}
```
