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AC academic-engineering-literature-survey

Engineering literature survey following Academic Research methodology. Two modes (initial survey / daily reading), two-phase workflow with user checkpoints, IEEE citations, structured briefs with tables and charts. Prioritizes IEEE Trans, Science Robotics, and reproducibility. Self-contained using web_search, web_fetch, sessions_spawn. For researchers who already know their direction and need to track field progress.

ClawHub Agent Skills author: Eshin v1.0.0 MIT-0 5 files body ≈ 2 467 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureResearchtype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 11 mutating operations with no state check
    • 40Consistency. Frontmatter name (academic-engineering-literature-survey) differs from the folder (paper-briefing-assistant)
    • 70Failures and branches. 7 branches
    • 85Steps. 46 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 2467 tokens
    • 100Progress reporting. Reports progress
    • low 11 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 420: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This is a transparent academic literature-survey skill that uses web research and optional memory in ways that fit its stated purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026