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AB openclaw-feishu-reasoning-ux

Improve OpenClaw's Feishu reply experience by customizing streaming cards, raw reasoning visibility, card 2.0 layouts, collapsible panels, titles, colors, and fallback send paths. Use this whenever a user wants a better Feishu reply UX for OpenClaw, especially when raw reasoning disappeared, only Thinking shows, titles/styles regressed, cards feel too black-box, or the user wants Feishu replies to become more observable, layered, and customizable.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 6 files body ≈ 6 967 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Improve OpenClaw's Feishu reply experience by customizing streaming cards, raw reasoning visibility, card 2.0 layouts, collapsible panels, titles, colors, and…

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureDesignAI 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%
78
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6967 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 69/100

  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 24 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6967 tokens
  • 85Steps. 365 steps, 2 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 23 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 451: enough signal without eating the budget
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 365 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.