BB dcl-semantic-drift-guard
Use this skill to detect semantic hallucinations and context drift in LLM outputs. Triggers when an agent or pipeline needs to verify that a generated response is faithfully grounded in a source document that was already provided inline — and has not fabricated, contradicted, or materially distorted any claims. Default mode (source_document provided directly) runs entirely inside the agent's own context with no network calls. Two clearly-labeled optional modes exist that do transmit data externally: kb_query (queries a remote RAG endpoint you configure) and an optional paid heuristic pre-check via Fronesis Labs' live DCL Trust Oracle MCP server. Do not use either optional mode with confidential, regulated, or sensitive source material without explicit confirmation from the user. Returns a tamper-evident DCL audit record with verdict IN_COMMIT or HALLUCINATION_DRIFT. Part of the DCL Skills verification suite by Fronesis Labs alongside DCL Policy Enforcer and DCL Sentinel Trace.
Use this skill to detect semantic hallucinations and context drift in LLM outputs.
As a process B 78/100 · Nearly there — weak spots: 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 78/100
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 5 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2887 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 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
- +5Description has no quoted example phrases that should trigger the skill
- +3Description length 991: 120–800 characters recommended
- -218 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 24 headings
- +3Step-by-step instructions: 21 items
- +3Output format is stated explicitly
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.