Belief State Runtime

结合规则层与 AI 判断评估声称是否可信：先获取证据，再对证据做六项布尔判断，由 assess.py 汇总为 0-1 的置信度及 VERIFIED/CONTESTED/UNCERTAIN 状态，供 Agent 判断是否引用该信息。

hqzzdsda

热度 21.69

最近更新 2026-06-08 15:14:52

分类：数据依赖：代码执行 / 内部调用大模型应用场景：研究 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add belief-state-runtime

Metadata

name: belief-state-runtime
description: LLM-driven epistemic reasoning engine. Evaluates claims against evidence, outputs calibrated confidence and structured belief state (VERIFIED/CONTESTED/UNCERTAIN). Use when the agent needs to assess whether information is trustworthy, detect contradictions in evidence, or quantify uncertainty.
version: 1.0.0
homepage: https://github.com/hqzzdsda/belief-state-runtime
metadata: {"clawdbot":{"requires":{"anyBins":["python3","python"]},"emoji":"🧠","os":["macos","linux"]}}

belief-state-runtime.skill

Evaluates the trustworthiness of a claim based on evidence.

When to use

User asks "is this true?" or "can I trust this?"
Agent collects information from multiple sources and needs to assess reliability
Detecting contradictions between evidence pieces
Quantifying confidence in a conclusion

⚡ AUTO WORKFLOW (Default - Always Run This First)

IMPORTANT: When this skill is triggered, you MUST follow this workflow automatically:

from assess import get_assessment_prompt, assess_claim_with_response

# Step 1: SEARCH FOR EVIDENCE (MANDATORY)
# Use your search tool to find real, verifiable evidence about the claim.

# Step 2: Get the assessment prompt
prompt = get_assessment_prompt(claim="The claim", evidence="Search results + user's evidence")

# Step 3: YOU (the AI) answer the prompt with 6 boolean judgments
# Based on your search results and analysis:
#   - direct_support: Does the evidence support the claim?
#   - new_info: Does it provide new information?
#   - logical_consistent: Is it logically consistent?
#   - direct_refute: Does it explicitly refute the claim?
#   - limitation: Are there limitations or exceptions?
#   - error_outdated: Is the claim outdated or wrong?
#
# Your answer format:
#   {"direct_support": true/false, "new_info": true/false, ...}

# Step 4: Get final result
result = assess_claim_with_response(
    claim="The claim",
    evidence="Search results + user's evidence",
    llm_response='{"direct_support": true, ...}'  # YOUR judgment
)

# Step 5: Present the result to the user

Workflow Summary

Step	Action	Tool/Function
1	Search for evidence	online-search / multi-search-engine
2	Get assessment prompt	`get_assessment_prompt(claim, evidence)`
3	Make 6 judgments	YOU (the AI)
4	Get result	`assess_claim_with_response(claim, evidence, llm_response)`
5	Present to user	Your response

How it works

Search for evidence (MANDATORY): Use search tools to find real, verifiable evidence.
Rule layer (Python): assess.py computes source reliability, evidence density, temporal freshness.
LLM layer (YOU): The AI agent answers 6 boolean questions about the evidence.
Aggregation (Python): Combines rule signals and your judgments into calibrated confidence.

Output

{
  "state": "VERIFIED",
  "confidence": 0.83,
  "confidence_range": [0.68, 0.98],
  "features": {"direct_support": true, ...},
  "summary": "Evidence strongly supports the claim"
}

States:

VERIFIED (confidence >= 0.65): Agent can cite this information
CONTESTED (0.25 < confidence < 0.65): Agent should note disagreement
UNCERTAIN (confidence <= 0.25): Agent should seek more information

Files

assess.py — self-contained skill with your custom domain/keyword/threshold/weight rules
config.json — your configuration in JSON format

External Endpoints

None. This skill is a pure computation engine. All evidence search is delegated to the host Agent.

Security & Privacy

No API keys required
No external network calls
No user data collection
All computation runs locally

Compatible with OpenClaw · Claude Code · Codex · Cursor · GitHub Copilot.

Customized via belief-state-runtime configurator