AC z1-matrix-memory-palace
Build and operate a file-driven Memory Palace for multi-agent systems, combining a spatial memory shell with a continuously maintained LLM Wiki reflection layer. Use when designing or deploying long-term memory architecture for agent teams, especially for (1) project rooms + task corridors + reflection wings, (2) low-token file-based coordination buses, (3) converting work artifacts into principles, prompt kernels, failure patterns, and thinking paths, (4) building a silent librarian/archivist agent, or (5) migrating scattered protocol/history files into a structured memory operating system. Includes source attribution and fusion guidance from Karpathy's llm-wiki idea and Jeff Pierce's memory-palace approach.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 98 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 895 tokens
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (10 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 718: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 98 items
- +4Reference files are cited in the instructions (5 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.