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AC fictiv

Operates Fictiv (app.fictiv.com), the on-demand manufacturing platform, end to end in the user's browser. Covers uploading CAD parts, configuring process, material, finish, threads, tolerances and inspections, getting instant or manual quotes, reading and fixing DFM feedback, choosing lead time and region, checking out and paying (card or PO), tracking orders, reordering, and troubleshooting. Applies when the user mentions Fictiv, wants a part CNC machined, 3D printed, sheet-metal fabricated, urethane cast, injection or compression molded, or die cast through an online service, asks to "get a quote" or "order parts" for a STEP/SLDPRT/STL file, wants to check a Fictiv quote or order status, or has a problem with a Fictiv upload, DFM warning, price or checkout. Also applies when the user wants custom parts manufactured and has a Fictiv account, even if Fictiv is not named.

K-Dense-AI/claude-scientific-skills Agent Skills author: K-Dense-AI MIT 10 files · 3 scripts body ≈ 3 236 tokens Open the sourcegithub.com↗ analyzed 12 h ago

Operates Fictiv (app.fictiv.com), the on-demand manufacturing platform, end to end in the user's browser. Covers uploading CAD parts, configuring process…

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureManufacturingCommerceWriting and documentstype 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 7 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 5 branches
    • 85Steps. 62 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3236 tokens

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 883: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.