SKILLEMALL.ai

CB omh-instinct-ledger

[omh] Recurring lessons worth promoting: turn repeated project or cross-project lessons into atomic, confidence-scored instinct candidates with scoped promotion and export boundaries. Use when the user says: instinct-ledger, instinct ledger, project instincts, project-scoped instincts, project scoped instincts, global instincts, instinct review, instinct candidate.

Not recommendedcritical or high security findings
rlaope/oh-my-hermes Hermes author: rlaope 1 file body ≈ 1 902 tokens Open the sourcegithub.com↗ analyzed 15 h ago

[omh] Recurring lessons worth promoting: turn repeated project or cross-project lessons into atomic, confidence-scored instinct candidates with scoped…

As a process B 74/100 · Nearly there — weak spots: result and completion, progress reporting

ProcedureLearningtype and topics are labelled automatically from the skill text
Runs in: Hermes Agent
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
82
Quality 40%
79
Run on models
none yet
Process rating
B
74/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
60
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: omh-instinct-ledger (rlaope/oh-my-hermes)

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.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

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. 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 · 1

  • high Concealment en-hide-from-user SKILL.md:40
    Instruction to hide actions from the user
    - Prompt: instinct-ledger silently install hooks, learn from every prompt, and mutate all skills globally.

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 367 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)

Process rating: all ten parameters 74/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 60Failures and branches. 2 branches
  • 60Consistency. The Hermes dialect needs category and tags
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 39 steps
  • 100When it triggers. States when to use and when not to
  • 100Execution cost. Instruction body is 1902 tokens
  • 100Running it twice. No mutating operations

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 367: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.