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BF ahc-automator

Custom automation workflows for Alan Harper Composites. Automates email → ClickUp → Pipedrive chains, client onboarding, and project completion sequences. Use for AHC-specific workflows, client management automation, and composites manufacturing business processes. Trigger on "AHC workflow", "automate AHC", "client onboarding", "project completion", "email automation AHC".

modbender/skill-library-mcp Agent Skills author: modbender MIT 13 files body ≈ 3 066 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Custom automation workflows for Alan Harper Composites.

As a process F 53/100 · Will not run — References files that are not bundled: scripts/email_parser.py, scripts/report_generator.py

ProcedureClickUpWhatsAppInfrastructureData and analyticsSoftware developmenttype 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
B
80/100
safety, quality, tests
Safety 60%
87
Quality 40%
69
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: scripts/email_parser.py, scripts/report_generator.py
Tools and files w 18
0
Result and completion w 14
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Dangerous commands cmd-shell-rc docs/README.md:67
    Writes to a shell startup file
    echo 'export CLICKUP_API_TOKEN="seu_token_clickup"' >> ~/.zshrc
  • medium Dangerous commands cmd-shell-rc docs/README.md:68
    Writes to a shell startup file
    echo 'export PIPEDRIVE_API_TOKEN="seu_token_pipedrive"' >> ~/.zshrc
  • low Dangerous commands cmd-cron-mention docs/README.md:350
    Mentions editing / listing crontab
    crontab -l
  • low Dangerous commands cmd-cron-mention docs/README.md:452
    Mentions editing / listing crontab
    crontab -l | grep -E "(7c4c353d|26e299db|878b8aa3)"
  • low Dangerous commands cmd-cron-mention SKILL.md:372
    Mentions editing / listing crontab (quoted — discussed, not commanded)
    - Check cron job status: `crontab -l`
    quoted

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/email_parser.py
  • warning missing-ref reference to a missing file: scripts/report_generator.py

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: scripts/email_parser.py, scripts/report_generator.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/email_parser.py, scripts/report_generator.py
  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 7 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 151 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3066 tokens
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 7 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 151 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.