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AC imsg-autoresponder

Monitor iMessage/SMS conversations and auto-respond based on configurable rules, AI prompts, and rate-limiting conditions. Use when you need to automatically reply to specific contacts with AI-generated responses based on conversation context. Also use when the user asks to manage auto-responder settings, contacts, prompts, or view status/history.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files · 1 script body ≈ 3 563 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Monitor iMessage/SMS conversations and auto-respond based on configurable rules, AI prompts, and rate-limiting conditions.

As a process C 62/100 · Has gaps — weak spots: result and completion, consistency, running it twice

GeneratorTelegramAI 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
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
79
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Consistency w 8
40
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Dangerous commands cmd-background-process scripts/launcher.sh:19
      Starts a background / autostarted process
      nohup node "$WATCHER_SCRIPT" >> "$LOG_FILE" 2>&1 &
    • low Dangerous commands cmd-background-process SKILL.md:57
      Starts a background / autostarted process
      nohup node ~/clawd/imsg-autoresponder/scripts/watcher.js > /dev/null 2>&1 &

    Files scanned: 9. 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 62/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 15 mutating operations with no state check
    • 40Consistency. Frontmatter name (imsg-autoresponder) differs from the folder (i-responder)
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 88 steps, 1 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 3563 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 16 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (45 tags): a typed call is more reliable

    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
    • -214 emoji in the instructions: noise for the model
    • -32 of 5 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 349: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 88 items
    • +4Has examples (14 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.