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AD tristan-rfq-overseer

Use this skill to run an end-to-end RFQ (Request for Quotation) pipeline for a procurement/sourcing operation: intake from email or Telegram, Obsidian-vault storage, pricing calculation, supplier quote comparison, and confirmed-send drafting. Trigger on the wake phrase "T, we live?", on inbound RFQ emails, on Telegram "/rfq" messages, or on "/price" and "/compare" commands.

ClawHub Agent Skills author: yrc96 v1.0.0 MIT-0 12 files body ≈ 1 060 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Use this skill to run an end-to-end RFQ (Request for Quotation) pipeline for a procurement/sourcing operation: intake from email or Telegram, Obsidian-vault…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureTelegramObsidianProcurementtype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

    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

    • note frontmatter-key unknown frontmatter key "codename"
    • note frontmatter-key unknown frontmatter key "wake_phrase"

    Process rating: all ten parameters 49/100

    • 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
    • 30Running it twice. 7 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1060 tokens
    • 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 376: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 23 items
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This skill is a disclosed RFQ workflow that uses an Obsidian vault, email, Telegram, and local pricing scripts without evidence of hidden execution, exfiltration, or automatic sending.
    LLM: benign (high) · VirusTotal: · 14 Jul 2026