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Write an engineering RFC (Request for Comments) for a technical decision, architectural change, or significant implementation approach. Use when asked to write an RFC, document a technical proposal, create a design doc, write an architecture decision for review, or produce a technical specification for team feedback. Produces a complete RFC document covering problem statement, motivation, proposed solution, alternatives rejected, implementation plan, migration plan, security and performance implications, observability changes, rollout plan, and open questions.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 3 899 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Write an engineering RFC (Request for Comments) for a technical decision, architectural change, or significant implementation approach.

As a process B 70/100 · Nearly there — weak spots: when it triggers, running it twice

GeneratorInfrastructuretype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
70/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: rfc-writer (mohitagw15856/pm-claude-skills)

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    • note edit-residue the text marks something as outdated (lines 174, 175): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 70/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 58 steps, 1 vague phrases
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3899 tokens
    • 100Progress reporting. Reports progress
    • low 18 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 566: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 58 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.