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BC decision-log

AI-powered decision journal for solopreneurs — capture decisions with context, rationale, and expected outcomes, then review them later to learn from what you got right and wrong.

ClawHub Hermes author: Clawdssen v1.0.2 MIT-0 3 files body ≈ 2 875 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerPersonal productivityInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent Cursor
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
Result and completion w 14
0
Inputs and preconditions w 11
0
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. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 179 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "url"

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 6 mutating operations with no state check
  • 40Consistency. Frontmatter name (decision-log) differs from the folder (agentledger-decision-log)
  • 50When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 8 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Execution cost. Instruction body is 2875 tokens
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 179: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (13 code blocks)
  • +1License stated

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

External checks

ClawHub: clean
This is a local decision-journal skill with optional automation prompts, and the reviewed artifacts do not show hidden code, network transfer, credential use, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026