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BC tetra-scar

Scar memory, reflex arc, and decision traces for AI agents. Learn from failures permanently. Block repeated mistakes instantly — no LLM calls needed. Three-layer memory: scars (immutable failures) + narrative (overwritable) + decision traces (judgment paths → LoRA training data).

ClawHub Agent Skills author: aibenyclaude-coder v0.4.0 MIT-0 10 files · 1 script body ≈ 746 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
71
Run on models
none yet
Process rating
C
56/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-labelled-token examples/example_incident_response.py:24
    Labelled token / key literal (vendor format unknown — verify it is not a live credential) (test fixture / example file; quoted — discussed, not commanded)
    "# config.py contains: API_KEY = 'sk-p…789'"
    fixturequoted
  • low Secrets in code secret-password-literal examples/example_incident_response.py:24
    Hard-coded password / key literal (may be an example) (test fixture / example file; quoted — discussed, not commanded)
    "# config.py contains: API_KEY = 'sk-p…789'"
    fixturequoted
  • low Dangerous commands cmd-privilege tetra_scar.py:196
    Privilege escalation / world-writable permissions (string literal in code, not executed)
    "sudo rm", "del /f /s",
    code literal

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "requires"

Process rating: all ten parameters 56/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 8 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 746 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 280: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill is mostly a disclosed local memory and safety-audit tool, but its CI action and broader repository-audit features deserve careful review before installation.
LLM: suspicious (high) · VirusTotal: · 29 May 2026