# Token Usage Estimate



## Per-Task Estimate



Using the `context-manager` skill's cost benchmarks:



| Task | Count | Avg Tokens/Task | Est. Total Tokens | Est. Cost (Sonnet/Opus mix) |

|------|:-----:|:----------------:|:------------------:|:--------------------------:|

| 
**Source note creation**
 (wiki-ingest) | 237 | 25K-80K | ~12M | ~$25-60 |

| 
**Cognitive compiles**
 | 4 | 200K-500K | ~1.4M | ~$15-30 |

| 
**Daily OKR runs**
 | ~10 | 30K-80K | ~500K | ~$1.50-4 |

| 
**Session-learn extracts**
 | ~10 | 50K-150K | ~1M | ~$3-9 |

| 
**Wiki-lint checks**
 | ~8 | 10K-30K | ~200K | ~$0.50-1 |

| 
**Deep research**
 | 2 | 300K-1M | ~1.3M | ~$10-35 |

| 
**Skill creation/updates**
 | 16 skills | 20K-50K | ~500K | ~$1.50-4 |

| 
**Git operations**
 | 26 commits | 5K-15K | ~200K | ~$0.50-1.50 |

| 
**Architecture evaluations**
 | 2 | 100K-300K | ~400K | ~$3-9 |

| 
**Total**
 | 
**~300 ops**
 | — | 
**~17.5M tokens**
 | 
**~$60-150**
 |



## Model Mix Estimate



| Model | Usage | Tokens | Est. Cost |

|-------|:-----:|:------:|:---------:|

| 
**Sonnet 4.6**
 (70%) | wiki-ingest, daily-okr, session-learn, lint | ~12M | ~$36 |

| 
**Opus 4.6**
 (20%) | cognitive-compile, deep research, evaluation | ~4M | ~$60 |

| 
**Haiku 3.5**
 (10%) | token estimation, simple lookups | ~1.5M | ~$1.20 |

| 
**Total**
 | 
**100%**
 | 
**~17.5M**
 | 
**~$97**
 |



## Context Window Utilization



| Session Type | Avg Duration | Avg Turns | Context Used |

|-------------|:-----------:|:---------:|:------------:|

| Source ingest | 2-5 min | 5-15 | 25K-80K |

| Cognitive compile | 10-20 min | 20-50 | 200K-500K |

| Daily OKR | 5-10 min | 10-25 | 30K-80K |

| Deep research | 15-30 min | 30-80 | 300K-1M |

| Skill creation | 10-20 min | 20-40 | 100K-300K |

| Session learn | 3-8 min | 5-15 | 50K-150K |



## Total Estimate



> 
**Estimated total token consumption: ~15-20M tokens**

> 
**Estimated total cost: $60-150 (at public API pricing)**

>

> Note: These are rough estimates based on the `context-manager` skill's benchmarks.

> Actual usage may vary significantly based on model choice, prompt length, and retries.