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AF pdf-rename

Rename academic PDF papers to a standardized format "[Year] [Venue] Title.pdf" using a three-stage pipeline (Extract → Verify → Rename). Use when the user asks to organize, batch-rename, or metadata-enrich PDF files in a folder. Activates on keywords like "rename PDFs", "organize papers", "batch rename PDFs", "rename papers by metadata", "pdf重命名", "文献整理".

ClawHub Agent Skills author: 67available v2.0.1 MIT-0 23 files body ≈ 1 049 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 44/100 · Will not run — References files that are not bundled: scripts/VERIFIED_DATA_*.py

ReferencePDFInfrastructureResearchtype 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
84
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: scripts/VERIFIED_DATA_*.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token scripts/VERIFIED_DATA_GAMETHEORY.py:123
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "2024…els for Sampled-Efficient RL.pdf": {
    quoted
  • low Secrets in code secret-high-entropy-token scripts/VERIFIED_DATA.py:140
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "2021…ion in Multi-Agent Competition.pdf": {
    quoted

Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/VERIFIED_DATA_*.py

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: scripts/VERIFIED_DATA_*.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/VERIFIED_DATA_*.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1049 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -312 of 16 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 357: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: suspicious
The skill mainly does PDF renaming, but it ships leftover document text/path state and optional LLM/file-renaming scripts that need review before installation.
LLM: suspicious (high) · 13 Jul 2026