SKILLEMALL.ai
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AC atifact

This skill should be used when the user asks to "extract agent trajectory", "convert HAR to trajectory", "get trajectory from session", "parse agent session", "convert Claude Code logs", "convert Copilot CLI logs", "extract ATIF", or needs to convert HAR files or JSONL session logs into ATIF trajectory JSON using the atifact CLI.

ClawHub Agent Skills author: Waldek Mastykarz v0.7.0 MIT-0 2 files body ≈ 944 tokens Open the sourceclawhub.ai↗ analyzed 15 d ago

This skill should be used when the user asks to "extract agent trajectory", "convert HAR to trajectory", "get trajectory from session", "parse agent session"…

As a process C 60/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
60/100
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: 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 60/100

    • 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
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 944 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 331: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a straightforward guide for converting agent session logs, but users should treat both the inputs and generated outputs as sensitive.
    LLM: benign (high) · VirusTotal: · 16 Sept 2026