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BF figma-use-figjam

This skill helps agents use Figma's use_figma MCP tool in the FigJam context. Can be used alongside figma-use which has foundational context for using the use_figma tool.

The skillemall take

This skill teaches agents to work with Figma MCP in FigJam context—the collaborative whiteboarding board. Promises to extend the foundational figma-use skill with FigJam-specific commands.

Grade B and quality score 76 suggest functional but imperfect state. No critical errors, but process score dropped to 32—the instructions are either incomplete or need refinement alongside figma-use. Broken references in files confirm structural gaps. Token count is low (1658), so context is narrow—FigJam only, nothing else.

Install if you already use figma-use and specifically need FigJam. Won't work standalone.

sst/opencode Claude Code author: sst MIT 14 files body ≈ 1 658 tokens Open the sourcegithub.com↗ analyzed 5 d ago

This skill helps agents use Figma's usefigma MCP tool in the FigJam context.

As a process F 32/100 · No process to follow — References files that are not bundled: ../figma-use/SKILL.md, ../figma-use/references/gotchas.md, ../figma-use/references/api-reference.md

ProcedureFigmaDesignAI 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
32/100
No process to follow
References files that are not bundled: ../figma-use/SKILL.md, ../figma-use/references/gotchas.md, ../figma-use/references/api-reference.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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: figma-use-figjam (anomalyco/opencode)

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../figma-use/SKILL.md
  • warning missing-ref reference to a missing file: ../figma-use/references/gotchas.md
  • warning missing-ref reference to a missing file: ../figma-use/references/api-reference.md

Process rating: all ten parameters 32/100

Will not run. References files that are not bundled: ../figma-use/SKILL.md, ../figma-use/references/gotchas.md, ../figma-use/references/api-reference.md
  • 0Tools and files. 3 referenced file(s) missing: ../figma-use/SKILL.md, ../figma-use/references/gotchas.md, ../figma-use/references/api-reference.md
  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 33 mutating operations with no state check
  • 85Steps. 19 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1658 tokens
  • 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 170: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (13 of 13)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.