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BD cocoindex

Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

davila7/claude-code-templates Agent Skills author: davila7 MIT 5 files body ≈ 6 300 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Comprehensive toolkit for developing with the CocoIndex library.

As a process D 38/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePostgreSQLAI 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
B
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
78
Run on models
none yet
Process rating
D
38/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: cocoindex (Lord1Egypt/RA-Skills)

How to improve

  1. 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv references/cli_operations.md:319
    Reads a .env file
    cat .env | grep COCOINDEX_DATABASE_URL

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6300 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 38/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Steps. 106 steps, 9 vague phrases
  • 70Execution cost. Instruction body is 6300 tokens
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low 10 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 402: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 106 items
  • +4Has examples (36 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.