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AC digest-comunidad-f100k

Módulo del Autopilot F100K que corre cada viernes: analiza los comentarios de los últimos 7 días en el Instagram/TikTok de la usuaria (Composio con Instagram profesional, Apify con datos públicos o capturas que ella manda), extrae las 5 preguntas más repetidas y los dolores más mencionados, y los convierte en ideas de contenido de alto valor con estructura de guion lista, que se suman a ideas/banco.md. Usar cuando alguien diga: "qué preguntan mis seguidores", "ideas desde mis comentarios", "qué quiere ver mi comunidad", "ejecutar digest comunidad", o cuando la automatización de los viernes lo invoque. El digest llega por Telegram. Requiere cerebro/f100k-config.json con ig_handle o tiktok_handle.

ClawHub Agent Skills author: Fórmula 100K v1.0.1 MIT-0 1 file body ≈ 1 470 tokens Open the sourceclawhub.ai↗ analyzed 32 h ago

Módulo del Autopilot F100K que corre cada viernes: analiza los comentarios de los últimos 7 días en el Instagram/TikTok de la usuaria (Composio con Instagram…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorTelegramMarketingAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 4 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1470 tokens
  • 100Running it twice. No mutating operations

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 4 example trigger phrases
  • +3Description length 704: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 4 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This skill has a coherent social-media digest purpose, but it handles connected account data and private comment identifiers with conflicting delivery instructions and weak user-control boundaries.
LLM: suspicious (high) · 17 Sept 2026