BC memphis
🔥 Memphis - Complete AI Brain for OpenClaw Agents ALL-IN-ONE meta-package with everything you need: 🧠 Core Features: - Local-first memory chains (journal, recall, ask, decisions) - Offline LLM integration (Ollama, local models) - Semantic search with embeddings - Knowledge graph - Encrypted vault for secrets 🚀 Cognitive Engine (Models A+B+C): - Model A: Record conscious decisions (manual) - Model B: Detect decisions from git (automatic) - Model C: Predict decisions before you make them (predictive) - 90.7% accuracy, proactive suggestions 🛠️ Setup & Management: - Bootstrap wizard (5-minute setup) - Self-loop capability (Memphis uses itself) - Auto-repair system - Chain monitoring - Backup automation 🌐 Multi-Agent Network: - Campfire Circle Protocol - Share chain sync (IPFS) - Multi-agent collaboration - Agent negotiation (trade protocol) Perfect for: Individual developers, teams, researchers, entrepreneurs Quick start: clawhub install memphis && memphis init
🔥 Memphis - Complete AI Brain for OpenClaw Agents ALL-IN-ONE meta-package with everything you need: 🧠 Core Features: - Local-first memory chains (journal…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 984 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "repository" - note
frontmatter-keyunknown frontmatter key "documentation"
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. 3 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2739 tokens
- low 19 top-level sections: this looks like several domains in one skill
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)
- +3Description length 983: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- -2localhost URLs: will not work for another user
- -258 emoji in the instructions: noise for the model
- +2Single-language instructions
- +4Structure: 59 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (28 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.