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CB semble-setup

Installs, audits, repairs, updates, enables, reindexes or removes the semble_code semantic code-search MCP for a project. Triggers: semble, semantic code search, semble status, настрой semble, статус semble, переиндексируй, удали semble.

kochetkov-ma/claude-brewcode Claude Code author: kochetkov-ma 61 files · 27 scripts body ≈ 8 740 tokens Open the sourcegithub.com↗ analyzed 7 h ago

Installs, audits, repairs, updates, enables, reindexes or removes the semblecode semantic code-search MCP for a project.

As a process B 65/100 · Nearly there — weak spots: result and completion, when it triggers, execution cost

AnalyzerAI and agentstype 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
C
83/100
safety, quality, tests
Safety 60%
93
Quality 40%
68
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Execution cost w 6
40
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.

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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Bash AskUserQuestion
  • low Dangerous commands cmd-pipe-to-shell-known-host scripts/semble-install.sh:115
    Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)
    raise precondition "brew is not installed - run this by hand: curl -LsSf https://astral.sh/uv/install.sh | sh"
    code literal

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 25. 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 ≈ 8740 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 418): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 65/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 8740 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 5 steps
  • 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 The response is described with custom markup (12 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
  • -225 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 237: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (20 code blocks)
  • +4Reference files are cited in the instructions (7 of 8)
  • +3All 9 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.