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AC ai-hallucination-remediation

Hallucination correction and AI hallucination remediation primary alias route into Official VeriClaw. If the real goal is the canonical public install surface, install `vericlaw` first; use this page to map AI hallucination, hallucination remediation, hallucination correction, AI幻觉纠偏, 幻觉纠偏, AI纠偏, and post-failure review wording back to the main skill.

ClawHub Agent Skills author: Sheygoodbai v0.1.7 MIT-0 3 files body ≈ 662 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "trigger"

    Process rating: all ten parameters 64/100

    • 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. 4 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 5 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 662 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

    • +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
    • +4No input/output examples
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 353: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 32 items
    • +1License stated

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

    External checks

    ClawHub: clean
    This skill is a low-risk routing alias that points hallucination-correction requests to VeriClaw without running code or accessing user data.
    LLM: benign (high) · VirusTotal: · 29 May 2026