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Точечное редактирование схемы компоновки данных 1С (СКД). Используй когда нужно модифицировать существующую СКД — добавить поля, итоги, фильтры, параметры, изменить текст запроса

Nikolay-Shirokov/cc-1c-skills Claude Code author: Nikolay-Shirokov MIT 3 files · 1 script body ≈ 4 833 tokens Open the sourcegithub.com↗ analyzed 12 h ago

Точечное редактирование схемы компоновки данных 1С (СКД).

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

Generator1CData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
90
Quality 40%
71
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
30
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

Obfuscation medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write Glob
  • medium Obfuscation uni-mixed-script-word SKILL.md:139
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (2 occurrences)
    Shorthand: `"OldName => NewName"`. Атомарно обновляет имя параметра, ссылки `&Имя` в выражениях других параметров (только полные совпадения, `&ПериодX` не задев…

Files scanned: 3. 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")

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 6 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4833 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 7 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (16 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +3Description length 178: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 7 items
  • +4Has examples (30 code blocks)
  • +2Bilingual instructions (RU + EN)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.