SKILLEMALL.ai
GitHub ↗ RU Sign in

CB address-feedback-with-replies

Address repo-owned feedback in Slack: inspect evidence, fix and verify accepted issues, and send concise in-thread status through the invoking user's connected Slack identity. Use when the user asks to address feedback.

BuilderIO/agent-native Agent Skills author: BuilderIO 1 file body ≈ 5 009 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Address repo-owned feedback in Slack: inspect evidence, fix and verify accepted issues, and send concise in-thread status through the invoking user's…

As a process B 75/100 · Nearly there — weak spots: result and completion, progress reporting

ProcedureSlackSoftware developmentInfrastructureData and analyticstype 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
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
Result and completion w 14
40
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5009 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "scope"

Process rating: all ten parameters 75/100

  • 0Progress reporting. Says nothing while it works
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5009 tokens
  • 85Steps. 33 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 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

  • +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
  • +4No input/output examples
  • -223 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 219: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 33 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.