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aiwg

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Deployment tool and support utility for AI context. Copies agents, skills, commands, rules, and behaviors into the paths each AI platform reads (Claude Code, Codex, Copilot, Cursor, Warp, OpenClaw, and 6 more) so one source of truth works across 10 platfo

aiwg.io

jmagly/aiwg

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# RLM Cost Report: {{tree_id}} **Generated**: {{timestamp}} **Status**: {{status_emoji}} {{status_text}} --- ## Summary | Metric | Value | |--------|-------| | **Total Tokens** | {{total_tokens}} ({{input_tokens}} in + {{output_tokens}} out) | | **Total Cost** | ${{total_cost_usd}} | | **Sub-Calls** | {{total_sub_calls}} | | **Max Depth** | {{max_depth}} | | **Duration** | {{duration}} | | **Budget Usage** | {{budget_usage_percent}}% | --- ## Budget Status ``` {{budget_bar}} ``` {{#budget_warnings}} ⚠️ **Warnings**: {{#warnings}} - {{.}} {{/warnings}} {{/budget_warnings}} {{#budget_projection}} 📊 **Projection**: Based on current trajectory, final cost estimated at **${{projected_cost}}** ({{projected_percent}}% of budget) {{/budget_projection}} --- ## Cost Breakdown by Depth | Depth | Nodes | Tokens | Cost (USD) | % of Total | |-------|-------|--------|------------|------------| {{#depth_breakdown}} | {{depth}} | {{node_count}} | {{tokens}} | ${{cost}} | {{percent}}% | {{/depth_breakdown}} | **Total** | **{{total_nodes}}** | **{{total_tokens}}** | **${{total_cost}}** | **100%** | --- ## Cost Breakdown by Model | Model | Calls | Tokens | Cost (USD) | Avg Cost/Call | |-------|-------|--------|------------|---------------| {{#model_breakdown}} | {{model}} | {{call_count}} | {{tokens}} | ${{cost}} | ${{avg_cost}} | {{/model_breakdown}} | **Total** | **{{total_calls}}** | **{{total_tokens}}** | **${{total_cost}}** | **${{avg_cost_per_call}}** | --- ## Percentile Analysis Based on REF-089 (Zhang et al., 2026) recursive decomposition cost patterns: | Metric | p25 | p50 | p75 | p95 | |--------|-----|-----|-----|-----| | **Cost per Sub-Call** | ${{p25_cost}} | ${{p50_cost}} | ${{p75_cost}} | ${{p95_cost}} | | **Tokens per Sub-Call** | {{p25_tokens}} | {{p50_tokens}} | {{p75_tokens}} | {{p95_tokens}} | ### Comparison to Alternative Approaches | Approach | Estimated Cost | Comparison | |----------|----------------|------------| | **Current (RLM)** | ${{total_cost_usd}} | — | | **Base Model (no decomposition)** | ${{base_model_cost}} | {{base_model_comparison}} | | **Summarization Approach** | ${{summarization_cost}} | {{summarization_comparison}} | {{#cost_efficiency_note}} 💡 **Note**: {{cost_efficiency_note}} {{/cost_efficiency_note}} --- ## Top 5 Most Expensive Nodes | Node ID | Model | Tokens | Cost (USD) | Prompt Preview | |---------|-------|--------|------------|----------------| {{#top_expensive_nodes}} | `{{node_id}}` | {{model}} | {{tokens}} | ${{cost}} | {{prompt_preview}} | {{/top_expensive_nodes}} --- ## Recommendations {{#recommendations}} ### {{category}} {{#items}} - **{{title}}**: {{description}} {{#metrics}} - _Expected Impact_: {{metric}} {{/metrics}} {{/items}} {{/recommendations}} --- ## Cost Breakdown Details ### Depth Distribution {{#depth_chart}} ``` Depth {{depth}}: {{bar}} {{node_count}} nodes (${{cost}}) ``` {{/depth_chart}} ### Model Usage Over Time {{#model_timeline}} - **{{timestamp}}**: {{model}} ({{tokens}} tokens, ${{cost}}) {{/model_timeline}} --- ## Configuration | Parameter | Value | |-----------|-------| | Max Depth | {{config_max_depth}} | | Max Sub-Calls | {{config_max_sub_calls}} | | Default Sub-Model | {{config_default_sub_model}} | | Budget Tokens | {{config_budget_tokens}} | | Parallel Sub-Calls | {{config_parallel}} | --- ## References - **Schema**: `@$AIWG_ROOT/agentic/code/addons/rlm/schemas/rlm-cost.yaml` - **Research**: REF-089 (Zhang et al., 2026) - Recursive Language Models - **Documentation**: `@$AIWG_ROOT/agentic/code/addons/rlm/docs/cost-analysis.md` --- **Report ID**: `{{report_id}}` **Tree ID**: `{{tree_id}}` **Generated by**: AIWG RLM Addon v{{addon_version}}