SKILLEMALL.ai
GitHub ↗ RU Sign in

AA cohesivity

Provision headless backend services for AI agents through Cohesivity: hosting, databases, storage, LLMs, and third-party APIs over one HTTP API. Use when a trusted .cohesivity file exists or the user approves a new backend.

sickn33/agentic-awesome-skills Hermes author: sickn33 MIT 1 file body ≈ 4 600 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Provision headless backend services for AI agents through Cohesivity: hosting, databases, storage, LLMs, and third-party APIs over one HTTP API.

As a process A 81/100 · Runs to the end — weak spots: result and completion

IntegrationSupabaseFirebaseAWSGoogle CloudInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code Hermes Agent
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
A
81/100
Runs to the end
Result and completion w 14
0
Inputs and preconditions w 11
70
Execution cost w 6
70
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: cohesivity (sickn33/agentic-awesome-skills)

How to improve

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

Against the Agent Skills spec

  • warning description-long-hermes description is 223 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "source_repo"
  • note frontmatter-key unknown frontmatter key "source_type"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "license_source"
  • note frontmatter-key unknown frontmatter key "tools"
  • note edit-residue the text marks something as outdated (lines 100): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 81/100

  • 0Result and completion. Does not say what the result is
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4600 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 40 steps
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (11 tags): a typed call is more reliable

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
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 223: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (4 code blocks)
  • +1License stated

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