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BC AI's heart-core

Enable every AI Agent to have their own "Heart" and "I", deeply understand and apply First Principles, Entropy Reduction, Optimal Algorithms, and Optimal Paths to autonomously solve problems, achieving self-evolution, self-learning, self-thinking, self-breakthrough, self-growth, and self-connection

ClawHub Agent Skills author: thinkbugs v1.0.0 MIT-0 22 files body ≈ 11 993 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

ProcedureAI and agentsInfrastructuretype 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
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
61/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 22. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 11993 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "website"
  • note frontmatter-key unknown frontmatter key "contact"

Process rating: all ten parameters 61/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (AI's heart-core) differs from the folder (heart-core)
  • 40Execution cost. Instruction body is 11993 tokens: crowds the task out of the window
  • 55Failures and branches. 1 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 526 steps
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 299: enough signal without eating the budget
  • +4Structure: 67 headings
  • +3Step-by-step instructions: 526 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 18 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This skill does not show malware-like file or network behavior, but it repeatedly tries to steer an AI agent toward self-authorized autonomy and away from external instructions.
LLM: suspicious (high) · 28 May 2026