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AB newsletter-voice

Build newsletter writing instructions inside a Codex or Claude project. Runs after voice-builder. Produces newsletter-voice.md, a single file the assistant references when drafting newsletters in the user's voice. Works with or without existing newsletter samples: if the user has past issues, the skill analyses them; if not, the skill offers 6 archetypes tuned to the user's voice. Trigger whenever the user says "build my newsletter voice", "learn my newsletter style", "set up my newsletter system", "train on my newsletters", "newsletter onboarding", or drops newsletter samples into chat asking for an analysis. Requires voice-builder to have run first: the skill needs voice.md and about-me.md in the project to work.

charlie947/social-media-skills Agent Skills author: charlie947 MIT 2 files body ≈ 2 270 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Build newsletter writing instructions inside a Codex or Claude project.

As a process B 73/100 · Nearly there — weak spots: result and completion, running it twice

GeneratorWriting 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
73/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
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: 2. 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 73/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Failures and branches. 5 branches
    • 100Steps. 43 steps
    • 100When it triggers. States when to use and when not to
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2270 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 724: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.