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AF long-form-article-writer

Write a 1500-3000 word long-form article in a chosen mode — editorial, personal essay, tech deep-dive, op-ed, newsletter, listicle, or Q&A interview. Auto-expands the brief into a 4-dimension narrative spec (hook, arc, voice, payoff), shows it back for review, then drafts one article or three alternative leads. Mirrors a pasted voice sample. Refuses to fabricate quotes or stats. Trigger when the user says "write a long-form article", "draft a blog post", "write a personal essay", "write an op-ed", "draft a tech deep-dive", or "write a feature article".

genspark Agent Skills author: Genspark 1 file body ≈ 2 954 tokens Open the sourcegenspark.ai↗ analyzed 2 d ago

As a process F 72/100 · Will not run — References files that are not bundled: assets/mode_picker.md, assets/voice_calibration_checklist.md, assets/fact_handling_protocol.md

GeneratorWriting and documentstype and topics are labelled automatically from the skill text
Runs in: Genspark 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
F
72/100
Will not run
References files that are not bundled: assets/mode_picker.md, assets/voice_calibration_checklist.md, assets/fact_handling_protocol.md
Tools and files w 18
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

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

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: assets/mode_picker.md
  • warning missing-ref reference to a missing file: assets/voice_calibration_checklist.md
  • warning missing-ref reference to a missing file: assets/fact_handling_protocol.md
  • warning missing-ref reference to a missing file: assets/narrative_spec_template.md
  • warning missing-ref reference to a missing file: assets/article_outline_skeletons.md

Process rating: all ten parameters 72/100

Will not run. References files that are not bundled: assets/mode_picker.md, assets/voice_calibration_checklist.md, assets/fact_handling_protocol.md
  • 0Tools and files. 5 referenced file(s) missing: assets/mode_picker.md, assets/voice_calibration_checklist.md, assets/fact_handling_protocol.md
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 7 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 75 steps
  • 100When it triggers. States when to use and when not to
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2954 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)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 558: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 75 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.