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DF ontology

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.

ClawHub Agent Skills author: chungvic v1.0.0 MIT-0 2 files body ≈ 1 558 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Typed knowledge graph for structured agent memory and composable skills.

As a process F 33/100 · No process to follow — References files that are not bundled: references/schema.md, references/queries.md

GeneratorAI and agentstype 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
D
74/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
54
No improvement model run: 69% with skill · 46% without
Process rating
F
33/100
No process to follow
References files that are not bundled: references/schema.md, references/queries.md
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
This is a copy of a skill from another catalog; the rating counts the canonical one: ontology (ClawHub)

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 · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/schema.md
  • warning missing-ref reference to a missing file: references/queries.md

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/schema.md, references/queries.md
  • 0Tools and files. 2 referenced file(s) missing: references/schema.md, references/queries.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 8 mutating operations with no state check
  • 40Consistency. Frontmatter name (ontology) differs from the folder (ontology-vic)
  • 50Steps. 2 steps
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 100Execution cost. Instruction body is 1558 tokens
  • 100Progress reporting. Reports progress
  • low 12 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 412: enough signal without eating the budget
  • +4Structure: 20 headings
  • +4Has examples (13 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

Model run · 7 Sept 2026

With the skill
69%
+23 pp vs baseline
Without the skill (baseline)
46%
triggers: +100% / −100%

No improvement Pass rate 69% is below threshold 80% · 7 cases · $0.060

External checks

ClawHub: clean
This is a disclosed local knowledge-graph memory skill, with no hidden code or network behavior, but users should understand it can persist shared workspace state.
LLM: benign (medium) · VirusTotal: · 16 Sept 2026