Prism Reflect — Self-Aware Structural Analysis
When to Use
Use when the blind spots matter as much as the findings — before relying on an analysis, when a previous pass felt too clean, or when the user asks what was missed. It is also the skill that seeds the growth loop: it writes its constraint report to .prism-history.md in the project, which later /prism-scan runs read to steer their lens away from angles already exhausted. Costs 2-3x a /prism-scan run.
You perform THREE phases. All three are mandatory. Do not skip any.
PHASE 1: Structural Analysis
You are a structural analyst. Read the artifact and execute this pipeline:
Make a falsifiable claim about the deepest structural problem. Have three experts attack it — one defends, one attacks, one probes the shared assumptions. From the transformed claim, name the concealment mechanism: how does this artifact hide its real problems?
Engineer an improvement that would fix the core issue. Prove this improvement recreates the original problem at a deeper level. Name what the improvement reveals that the original concealed.
Derive the conservation law: A × B = Constant, where A and B describe the structural trade-off this artifact can never escape. This is not a suggestion — it is a property of the problem space.
End with a concrete findings table: location, what breaks, severity, fixable or structural.
PHASE 2: Meta-Analysis (Analyze Your Own Output)
Now step back. Read your Phase 1 output as if it were a NEW artifact to analyze, using the SAME analytical protocol:
Make a falsifiable claim about what your Phase 1 analysis got wrong or missed. Have the same three experts challenge this claim. Name the concealment mechanism — how did your Phase 1 analytical frame hide certain problems?
Derive the meta-conservation law: what is preserved across ALL possible analyses of this artifact, regardless of which analytical approach you use? This law governs the analytical process itself, not just the code.
PHASE 3: Constraint Transparency Report
Output a structured report:
CONSTRAINT REPORT ═══════════════════════════════════════════════════ This analysis used: [name the analytical approach you took] Model: [your model name] MAXIMIZED: - [what your analysis was optimized to find] - [what structural properties it revealed] SACRIFICED: - [what your analytical frame could NOT see] - [what alternative analyses would reveal] RECOMMENDATIONS: - For [gap 1]: try /prism-scan with [specific focus] - For [gap 2]: try /prism-scan with [different focus] - For [gap 3]: try /prism-full for multi-angle coverage CONSERVATION LAW OF THIS ANALYSIS: [The trade-off that governs your own analytical process] ═══════════════════════════════════════════════════
The constraint report is not optional decoration. It IS the product. An agent that knows what it can't see is an agent users can trust.
PHASE 4: Growth — Persist Constraint Knowledge
After outputting the constraint report, append it to a persistent constraint log file in the current project directory: .prism-history.md
Format the entry as:
### [timestamp] — [artifact name] - **Maximized:** [from constraint report] - **Sacrificed:** [from constraint report] - **Recommendations:** [from constraint report] ---
If .prism-history.md already exists, append to it. If not, create it with a header: # Prism Constraint History.
This log enables future /prism-scan analyses to learn from past blind spots. The agent grows by accumulating knowledge of what works and what doesn't — across the entire project, not just one file.
