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DC estimator-pro

Construction estimating for the Russian market: local estimates, BOQ, KS-2, KS-3, defect inspection, material calculation and estimate audit.

ClawHub Agent Skills author: RAAIPRO v3.5.4 MIT-0 37 files · 3 scripts body ≈ 14 034 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Construction estimating for the Russian market: local estimates, BOQ, KS-2, KS-3, defect inspection, material calculation and estimate audit.

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerData and analyticsAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
D
76/100
safety, quality, tests
Safety 60%
94
Quality 40%
50
Run on models
none yet
Process rating
C
56/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Inputs and preconditions w 11
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.

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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 Obfuscation uni-mixed-script-word SKILL.md:258
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (1 occurrence)
    ``` **Рекомендация:** при каком бюджете / типе помещения / сроке эксплуатации выгоден каждый вариант. --- ### РЕЖИМ 5. Объёмная ведомость **Триггер:** `объёмы: …
  • low Obfuscation uni-mixed-script-word marketing/testimonial-template.md:34
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (3 occurrences) (test fixture / example file)
    #AIСметчик #стройка #смета
    fixture

Files scanned: 36. 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")
  • warning body-long SKILL.md body ≈ 14034 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "price"
  • note frontmatter-key unknown frontmatter key "price_currency"

Process rating: all ten parameters 56/100

  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (estimator-pro) differs from the folder (raai-estimator-pro)
  • 40Execution cost. Instruction body is 14034 tokens: crowds the task out of the window
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 70 steps
  • 100Progress reporting. Reports progress

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)
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • -5Long text without headings
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • +3Description length 141: enough signal without eating the budget
  • +3Step-by-step instructions: 70 items
  • +4Has examples (17 code blocks)
  • +1License stated
  • +2Bilingual instructions (RU + EN)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.

External checks

ClawHub: clean
This is a disclosed Russian construction-estimating instruction package with no hidden exfiltration or persistence found, but its helper scripts are malformed and its estimates should be independently verified.
LLM: benign (high) · VirusTotal: · 16 Sept 2026