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BD token-stats-skill

Record and analyze local token usage from Codex, Claude Code, OpenClaw, Hermes Agent, OpenCode, DeepSeek Harness, WorkBuddy, and WorkBuddy AI with the published token-stats CLI. Use for source diagnostics, daily snapshots, historical date-range reports, agent/model/session breakdowns, or periodic local collection with launchd or cron. Reports reflect available local usage counters, not billing charges.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: baijian v0.0.2 MIT-0 6 files · 2 scripts body ≈ 1 834 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Record and analyze local token usage from Codex, Claude Code, OpenClaw, Hermes Agent, OpenCode, DeepSeek Harness, WorkBuddy, and WorkBuddy AI with the…

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAI and agentsSoftware developmentData and analyticstype 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
82/100
safety, quality, tests
Safety 60%
77
Quality 40%
89
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.

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.
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 scripts/install-launchd-token-recorder.sh:230
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl bootstrap "gui/$uid" "$plist_path"
Medium and low: 1
  • medium Dangerous commands cmd-persistence references/token-recording.md:120
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (quoted — discussed, not commanded)
    launchctl bootout "gui/$(id -u)" "$HOME/Library/LaunchAgents/com.baijian.ai-token-ayalysis.plist"
    quoted

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

Process rating: all ten parameters 45/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1834 tokens
  • 100Progress reporting. Reports progress

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
  • +2Single-language instructions
  • +3Description length 405: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

External checks

ClawHub: suspicious
The skill is transparent about local token analytics, but it relies on an opaque external binary that can read broad private AI session data and can install a persistent recorder.
LLM: suspicious (high) · 9 Sept 2026