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BF insight-engine

Logs/metrics → Python statistics → LLM interpretation → Notion reports. Use when: generating daily/weekly/monthly operational insights from AI system logs, producing data-driven Notion reports from Langfuse traces and gateway logs, setting up a cron-based insight pipeline, building a citation-enforcing analyst that refuses to make claims without specific data. Pattern: collect raw data → compute stats in Python → feed structured packet to LLM → write to Notion.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Nissan Dookeran v1.0.4 MIT-0 15 files body ≈ 784 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process F 33/100 · Will not run — References files that are not bundled: scripts/config/analyst.yaml.example

ProcedureNotionData and analyticsSoftware developmentAI and agentstype 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
78/100
safety, quality, tests
Safety 60%
77
Quality 40%
80
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: scripts/config/analyst.yaml.example
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The text references files that are not there: add them or drop the references.
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 · 2

  • high Dangerous commands cmd-persistence SKILL.md:90
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    <!-- ~/Library/LaunchAgents/com.yourname.insight-engine-daily.plist -->
Medium and low: 1
  • medium Exfiltration net-redirectable-api-key scripts/src/engine.py:49
    Helper sends the API key to a host configured by an environment variable — the key can be redirected to another server
    API key + configurable base URL from environment

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/config/analyst.yaml.example

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: scripts/config/analyst.yaml.example
  • 0Tools and files. 1 referenced file(s) missing: scripts/config/analyst.yaml.example
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 100Steps. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 784 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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 465: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: suspicious
This skill mostly does what it says, but it can send sensitive local memory and repository context to Anthropic and Notion more broadly than its security notes imply.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026