SKILLEMALL.ai

AC expertpack-export

Export an OpenClaw instance's accumulated knowledge into a structured ExpertPack composite. Use when backing up an agent's identity, exporting for migration, or creating a portable knowledge snapshot. Handles auto-discovery (scanning workspace state to identify constituent packs), distillation (compressing raw state into structured EP files), and packaging (writing EP-compliant packs + composite manifest). Output is Obsidian-compatible — includes YAML frontmatter on all content files and can be opened as an Obsidian vault. NOT for importing/hydrating from an existing EP.

ClawHub Agent Skills author: Brian Hearn v1.1.0 MIT-0 7 files body ≈ 888 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

GeneratorObsidianInfrastructureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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: 7. 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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 24 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 888 tokens

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 577: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 24 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: suspicious
    This is a coherent export tool, but it should be reviewed because it scans sensitive agent/workspace knowledge and overstates its automatic redaction and formatting guarantees.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026