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samuelgursky/davinci-resolve-mcp Agent Skills author: samuelgursky MIT 80 files body ≈ 38 765 tokens Open the sourcegithub.com↗ analyzed 32 h ago

This document gives AI assistants the context needed to use the DaVinci Resolve MCP server effectively.

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

ProcedureAI and agentsMedia and videotype 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
D
58/100
safety, quality, tests
Safety 60%
97
Quality 40%
0
Run on models
none yet
Process rating
B
65/100
Nearly there
Execution cost w 6
10
When it triggers w 12
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

  1. Add a description to the frontmatter: without it the skill never triggers.
  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
  • low Secrets in code secret-high-entropy-token reference/evidence/roundtrip-gui-20260801.json:124
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "name": "RT_f…rce",
    quoted
  • low Secrets in code secret-high-entropy-token reference/evidence/roundtrip-headless-20260801.json:124
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "name": "RT_f…rce",
    quoted
  • low Secrets in code secret-high-entropy-token reference/resolve_scripting_api.txt:1149
    High-entropy token-like string (may be an id, hash or a credential)
    GetSetting and SetSetting                       --> {}                 # settingName vide…SDI is now replaced with vide…SDI and videoMonitorMatrixOv

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error frontmatter SKILL.md: no YAML frontmatter block found
  • error name-missing SKILL.md: frontmatter has no `name`
  • error description-missing SKILL.md: no `description` — the skill can never trigger
  • warning body-long SKILL.md body ≈ 38765 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 225, 228, 1594, 1993, 2647): 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

  • 10Execution cost. Instruction body is 38765 tokens: crowds the task out of the window
  • 30When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 275 steps, 3 vague phrases
  • 100Failures and branches. 3 branches, has a failure section
  • 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 16 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (5 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)
  • +3Description length 0: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 40 headings
  • +3Step-by-step instructions: 275 items
  • +4Has examples (25 code blocks)
  • +4Reference files are cited in the instructions (13 of 23)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 0.