SKILLEMALL.ai

BD phy-dotenv-inheritance-mapper

Maps your full .env inheritance chain into a single resolved view. Reads all .env files (.env, .env.local, .env.example, .env.staging, .env.production, .env.test) in the correct override order, shows which layer each variable comes from, flags conflicts between environments, detects variables missing from .env.example, and identifies variables referenced in code that have no definition anywhere. Works for any framework or language. Zero external API — pure local file analysis. Triggers on "env inheritance", "which env file wins", "merge env files", "effective env values", "env override order", ".env chain", "/dotenv-mapper".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI 2 files body ≈ 3 728 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Maps your full .env inheritance chain into a single resolved view. Reads all .env files (.env, .env.local, .env.example, .env.staging, .env.production…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedurePostgreSQLDockerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
49/100
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

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note edit-residue the text marks something as outdated (lines 298): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 49/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 7 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, git) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3728 tokens
  • 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -212 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 632: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (16 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.