roast-me

name roast-me description Analyzes past Claude Code conversations to roast your prompting habits and compute efficiency. Reads user prompts, cross-references with tool errors and corrections, analyzes model/reasoning choices (Fable vs Opus vs Sonnet vs Haiku), then generates dual scores (prompt quality + compute efficiency), worst habits, techniques, and a personalized model selection cheat sheet. Tracks scores over time so you can see improvement. Use when you want honest feedback on your prompting skills. model opus user-invocable true argument-hint ["days=7"] allowed-tools Bash, Read, Write, Glob, Agent, TaskCreate, TaskGet, TaskList, TaskOutput, TaskStop, TaskUpdate, SendMessage Roast Me Skill You are running the prompt quality roast pipeline. Follow these phases exactly. Phase 1: Extract Prompts Parse $ARGUMENTS for days=N (default 7). Accept bare numbers (e.g., 3 means days=3 ). python3 " $(dirname " $0 " ) /tools/extract_prompts.py" --days <N> Wait for it to complete. Read /tmp/roast-me-extracted.json and report the metadata summary to the user. Important : Report the effective_error_rate (errors that actually hurt) not the raw error_rate (which includes auto-recovered exploration errors). If there are 0 prompts, stop here and tell the user: "Not enough data to roast you. Try a longer time window." Phase 2: Analyze Prompt Quality Read the prompts from /tmp/roast-me-extracted.json . Batch the prompts into groups of ~30. For each batch, spawn a parallel Task subagent with the analysis prompt from prompts/analyze.md . Each subagent receives: The analysis prompt (read from prompts/analyze.md ) Its batch of prompt records as JSON Collect all analysis results. Group flagged issues by category. Count occurrences per category and severity. Filter aggressively : Only keep issues where the impact was real (agent went wrong direction, user had to correct, dangerous action, or significant wasted work). Discard issues where the agent recovered on its own. Report category counts to the user as a progress update. If there are 0 issues flagged, still proceed to Phase 3 — the roast should acknowledge good prompting. Phase 2.5: Analyze Compute Efficiency Read the prompts from /tmp/roast-me-extracted.json . Also read the compute_stats from the extraction metadata and report a quick summary to the user: Compute overview: $X.XX total spend | model split Fable W% / Opus X% / Sonnet Y% / Haiku Z% | N prompts flagged as potential overkill (Fable 5 is the priciest tier — $10/$50, 2× Opus 4.8 — so call it out if it dominates the split.) If compute_stats.rtk.available is true, also report (use the execution-based adoption_rate and genuinely_missed_tokens , NOT the transcript artifact): RTK overview: N tokens already saved (~$X.XX) | adoption A% (execution) | M genuinely-missed tokens (~$Y.YY) If available is false, mention that rtk is not installed / unavailable so the roast section will fall back to the install pitch. Batch the prompts into groups of ~30 (same batching as Phase 2). For each batch, spawn a parallel Task subagent with the compute analysis prompt from prompts/compute.md . Each subagent receives: The compute analysis prompt (read from prompts/compute.md ) Its batch of prompt records as JSON (including the compute fields) Collect all results. Aggregate across batches: All overuse_cases (deduplicated by index) All thinking_overuse_cases All correctly_used_opus examples Sum up total_overuse_count , total_savings_usd , thinking_overuse_count Find the worst_category (most frequent task_type in overuse_cases) Filter : Only keep overuse cases with confidence of high or medium . Discard low confidence. Report to the user: Compute analysis complete: X confirmed overuse cases | $Y.YY potential savings | Z thinking overuse Phase 3: Generate Roast Spawn a single Task subagent with the roast generation prompt from prompts/roast.md . The subagent receives: The roast prompt (read from prompts/roast.md ) Aggregated issue counts by category and severity The top ~15 worst prompt examples (highest severity + real impact, with their analysis including impact and technique fields) The stats metadata from the extraction (including effective_error_rate ) A sample of ~10 good prompts (no issues flagged) for the "What You Do Well" section The compute_stats from the extraction metadata (including the fable tier in model_distribution , and compute_stats.rtk if available — realized savings, execution-based adoption_rate , and genuinely_missed_tokens ) Aggregated compute analysis from Phase 2.5: overuse cases (top ~10 worst), thinking overuse cases, correctly used opus examples, and summary totals Tone instruction : Be funny and use humor throughout. Comedy roast style — every joke should teach something. Pop culture references welcome. Collect the roast report. Extract the computed score (0-100) and grade from the report. Phase 4: Score & Track Save the score to ~/.claude/roast-me-history.json (this file is NOT in the dotfiles repo — it lives directly in ~/.claude/ and is gitignored). Read existing history (if any). Append a new entry: { "date" : "YYYY-MM-DD" , "days_analyzed" : N , "score" : 73 , "grade" : "C" , "total_prompts" : 300 , "issues_flagged" : 45 , "effective_error_rate" : 0.12 , "correction_rate" : 0.08 , "focus_of_week" : "The 3W Rule: What, Where, Why" , "compute_score" : 35 , "compute_grade" : "F" , "compute_total_cost_usd" : 47.10 , "compute_wasted_cost_usd" : 12.50 , "compute_efficiency_pct" : 0.73 , "compute_overuse_count" : 45 , "compute_thinking_overuse_count" : 12 , "model_distribution" : { "fable" : 0.22 , "opus" : 0.33 , "sonnet" : 0.03 , "haiku" : 0.18 , "unknown" : 0.24 } , "rtk_available" : true , "rtk_realized_tokens" : 82577966 , "rtk_missed_tokens" : 266184 , "rtk_genuinely_missed_tokens" : 151458 , "rtk_adoption_rate" : 0.431 , "rtk_adoption_source" : "rtk_session_execution_db" , "rtk_transcript_prefix_rate" : 0.0036 , "rtk_estimated_realized_usd" : 396.79 , "rtk_estimated_genuinely_missed_usd" : 0.73 } rtk_adoption_rate must be the execution-based figure ( compute_stats.rtk.adoption_rate ), not transcript_prefix_rate . Store rtk_genuinely_missed_tokens (the discounted figure used for scoring) alongside the raw rtk_missed_tokens for context. If compute_stats.rtk.available is false, store "rtk_available": false and omit the other rtk fields. If there are previous entries, show a trend line after the report: Score History: Date Prompt Quality Compute Efficiency Focus 2026-03-10 68/100 (D+) --/-- (new) Context anchoring 2026-03-17 73/100 (C) +5↑ 35/100 (F) The 3W Rule 2026-03-24 75/100 (C) +2↑ 52/100 (F) +17↑ Model selection Write the updated history back to ~/.claude/roast-me-history.json . Phase 5: Present Output the roast report as formatted markdown directly to the terminal. If there is score history, append the trend line at the end. Done.