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CD deeptutor-cli

Configure, manage, and use DeepTutor through its CLI, including capabilities, knowledge bases, partners, memory, sessions, notebooks, providers, skills, and the server or Web app.

HKUDS/DeepTutor Agent Skills author: HKUDS Apache-2.0 80 files body ≈ 2 749 tokens Open the sourcegithub.com↗ analyzed 24 h ago

Configure, manage, and use DeepTutor through its CLI, including capabilities, knowledge bases, partners, memory, sessions, notebooks, providers, skills, and…

As a process D 47/100 · Unfinished process — References files that are not bundled: docs-for-user/AGENT_SETUP.md, docs-for-user/AGENT_SETUP.md

IntegrationSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
D
47/100
Unfinished process
References files that are not bundled: docs-for-user/AGENT_SETUP.md, docs-for-user/AGENT_SETUP.md
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token assets/README/README_FR.md:41
    High-entropy token-like string (may be an id, hash or a credential)
    [Fonctionnalités](#-fonctionnalités-clés) · [Démarrage](#-démarrage) · [Explorer](#-explorer-deeptutor) · [CLI](#%EF%B8%8F-d…ive-à-lagent) · [Écosystème](#-écosystème--eduhub

Files scanned: 78. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: docs-for-user/AGENT_SETUP.md
  • warning missing-ref reference to a missing file: docs-for-user/AGENT_SETUP.md
  • note edit-residue the text marks something as outdated (lines 22): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 47/100

Will not run. References files that are not bundled: docs-for-user/AGENT_SETUP.md, docs-for-user/AGENT_SETUP.md
  • 0Tools and files. 2 referenced file(s) missing: docs-for-user/AGENT_SETUP.md, docs-for-user/AGENT_SETUP.md
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (deeptutor-cli) differs from the folder (DeepTutor)
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 12 steps
  • 100Execution cost. Instruction body is 2749 tokens
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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 179: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 12 items
  • +4Has examples (16 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.