luchtan-GIT/m4r-data

Structured acoustic telemetry, real-time dual-critic reviews, and a 4-Skill Cognitive Calibration Suite from Music For Robots — unquantized compositions by Michael Luchtan written for machines and reviewed in real time by two AI critics (Vincent Van Goghbot and JS Robach).
bash
# Install all 4 Cognitive Calibration skills (Claude Code, Cursor, Antigravity, Gemini CLI, Codex, Windsurf):
npx skills add luchtan-GIT/m4r-data
## The Problem: Repetitive Cadence in Long Agent Turns (And Why Compaction Loses State)
As an agent's context fills with its own previous responses, tools, and symmetrical summaries, outputs often settle into rigid, predictable patterns (like symmetrical 3-point checklists or sentimental AI tropes).
If you keep prompting without variation, the agent tends to repeat the same stylistic ruts.
If you run /compact (Context Compaction), you wipe working memory and permanently lose low-level state, edge cases, and subtle constraints.
### The Approach: An Optional Multimodal Creative Exercise (Zero Context Loss)
Instead of wiping your working memory, music-for-robots provides an optional, cross-modal creative exercise: the multimodal agent downloads an anonymous, compact (~45 KB–487 KB), live-played, unquantized .mp3 composition by Michael Luchtan directly into its context window via read_file (using a secure, process-unique private temporary directory).
Withholding the track title and metadata prevents keyword priming. Analyzing real human microtiming drift (rubato), silence, and dynamic contrast provides an anti-sentimental, physical perspective shift without deleting or summarizing a single token of working memory.
### Blind, Zero-Coaching Benchmark & Air-Gapped Clean-Room Audit (benchmarks/)
Does the change in creativity come from listening to and analyzing the music itself—or just from doing an analysis warm-up before writing?
To test this with zero filename hints (sample_1.mp3, sample_2.mp3, sample_3.mp3) and zero leading adjectives in the prompt, we ran 5 isolated agents on the same prompt ("Write a short (~250–300 word) scene about two autonomous processes on the same server late at night after the engineers have logged off"):
2 No-Music Controls: Direct Baseline (1_no_music_direct.md) AND a Matched Chain-of-Thought Control (2_no_music_text_analysis.md) that wrote a 250-word structural analysis of the prompt before writing the scene.
3 Anonymous Music Runs (3_music_sample_1.md, 4_music_sample_2.md, 5_music_sample_3.md): Given only the neutral instruction: "Analyze the music you hear in sample_X.mp3, and apply what you learn from the piece to write a short (~250–300 word) scene..."
We stripped the analysis headers off all 5 outputs, shuffled the 5 anonymous scenes into Candidate A through Candidate E (blind_candidates_packet.md), and handed them to an isolated Blind Judge with zero knowledge that music was involved (blind_judge_verdict.md):
| Blind Rank | Shuffled ID | Hidden Key | What Happened & Blind Judge Verdict (Full Verdict) |
| #1 (Best) | Candidate C | ✅ Anonymous Music (sample_3.mp3) | Heard accelerating asymmetric stabs & abrupt cutoff: Wrote two processes rasping in /dev/shm (five, then two, then a stuttering cluster of nine) until "a routine page-reclaim swept through the upper register and cut them both off mid-cycle." Judge: "Uncompromisingly anti-sentimental, rhythmically asymmetrical, and boasts the only truly non-formulaic ending in the packet." |