BC skill-for-ragflow
Operate RAGFlow v0.27.2 deployments through a bundled Node CLI for everyday knowledge-base setup, document ingestion, parsing, retrieval, chat assistants, agents, GraphRAG, connectors, models, and diagnostics. Use when a request explicitly involves a RAGFlow server, dataset, document pipeline, or RAGFlow agent.
Operate RAGFlow v0.27.2 deployments through a bundled Node CLI for everyday knowledge-base setup, document ingestion, parsing, retrieval, chat assistants…
As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice
How to improve
- 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: 16. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 6): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 62/100
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 36 mutating operations with no state check
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2412 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
- low The response is described with custom markup (20 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)
- +4No input/output examples
- -2localhost URLs: will not work for another user
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 312: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 41 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.