SKILLEMALL.ai

AD context-manager-agentic

Verify and distribute trust-gated context (memory and skill files) to a multi-agent roster before it's injected. Use when assembling context for several sub-agents from shared memory/skill files, to catch files silently modified since they were last verified.

ClawHub Agent Skills author: Abdoul Anzize Daouda v1.0.0 MIT-0 2 files body ≈ 322 tokens Open the sourceclawhub.ai analyzed 2 d ago

Verify and distribute trust-gated context (memory and skill files) to a multi-agent roster before it's injected.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 39/100

    • 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
    • 50Steps. 2 steps
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 322 tokens
    • 100Running it twice. No mutating operations

    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)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 259: enough signal without eating the budget
    • +4Structure: 3 headings
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    The skill is a coherent context trust-gating helper that reads configured memory and skill files, verifies hashes, and distributes scoped bundles without hidden privileged behavior in the inspected artifacts.
    LLM: benign (high) · VirusTotal: · 3 Aug 2026