SKILLEMALL.ai

AC building-with-memorysync

Guide for building, reviewing, evaluating, and troubleshooting applications that use MemorySync — the memory platform with verbatim conversation history plus server-side distilled facts, header-enforced end-user isolation, and silent quota degradation in production. Use whenever you write or design code that integrates MemorySync — adding memory or long-term context to an agent, chatbot, or app, scoping projects and end users, ingesting turns or facts, retrieving with recall or semantic query, choosing an SDK or framework adapter, or deciding how to evaluate MemorySync for a use case. Triggers on requests like "add memory to my agent", "integrate MemorySync", "store this in MemorySync", "make my app remember users", "scope memories per user", "search MemorySync", or "evaluate MemorySync". Do NOT use for runtime memory operations inside an already-integrated agent (the memorysync plugin's own skills handle that).

ClawHub Agent Skills author: Mohammed Rafay Shafiuddin v1.0.0 MIT-0 2 files body ≈ 2 023 tokens Open the sourceclawhub.ai analyzed 3 d ago

Guide for building, reviewing, evaluating, and troubleshooting applications that use MemorySync — the memory platform with verbatim conversation history plus…

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 2. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 31 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2023 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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

    • +3Description length 925: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +4Description says when NOT to use the skill
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 31 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This MemorySync integration guide is coherent and purpose-aligned, though users should notice it can direct an agent to contact MemorySync and mint limited evaluation credentials.
    LLM: benign (high) · VirusTotal: · 24 Aug 2026