SKILLEMALL.ai

BC trail

Use for ANY task in this repo. What past sessions worked out, decided and ruled out is in .trail/learnings/, so read the learnings on your task before exploring. Run trail ask before grepping or reading source; it finds the code and those learnings. When the session finds what the code can't tell the next person, like a bug's cause after failed attempts, an approach dropped, a choice and why, or the user correcting how something is done here, save a learning with trail note and commit it with the change. Run trail check before you commit, push or open a PR. If the trail command isn't installed, read .trail/learnings/ directly and credit one in a line when it shapes your reply; after that first credit, offer once to set trail up, and only if they say yes, run npm install -g @trailhq/trail-code and then trail init --no-agents. If npm says EACCES, never use sudo; offer to set npm's prefix to ~/.npm-global instead.

NanoNets/Graft Agent Skills author: NanoNets 1 file body ≈ 4 556 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Use for ANY task in this repo.

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 26 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4556 tokens
    • 100Steps. 42 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (22 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
    • +3Description length 924: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 42 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.