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DC tech-news-digest

Generate tech news digests with unified source model, quality scoring, and multi-format output. Six-source data collection from RSS feeds, Twitter/X KOLs, GitHub releases, GitHub Trending, Reddit, and web search. Pipeline-based scripts with retry mechanisms and deduplication. Supports Discord, email, and markdown templates.

Not recommendedcritical or high security findings · low grade D
LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 38 files · 18 scripts body ≈ 4 793 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Generate tech news digests with unified source model, quality scoring, and multi-format output.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

GeneratorDiscordGitHubTelegramInfrastructuretype 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
D
77/100
safety, quality, tests
Safety 60%
82
Quality 40%
70
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: openclaw-master-skills, openclaw-master-skills

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Broad scope
If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

  • high Broad scope meta-requests-env-secret SKILL.md:1
    Skill asks the runtime to inject credential env vars into its sandbox: X_BEARER_TOKEN, TWITTERAPI_IO_KEY, TAVILY_API_KEY, BRAVE_API_KEYS, BRAVE_API_KEY, GITHUB_TOKEN, GH_APP_KEY_FILE (high-value: GITHUB_TOKEN, GH_APP_KEY_FILE) — verify each one is needed for the stated purpose
    required_environment_variables: X_BEARER_TOKEN, TWITTERAPI_IO_KEY, TAVILY_API_KEY, BRAVE_API_KEYS, BRAVE_API_KEY, GITHUB_TOKEN, GH_APP_KEY_FILE

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "files"

Process rating: all ten parameters 56/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 40Consistency. Frontmatter name (tech-news-digest) differs from the folder (xiaoding-dinstein-tech-news-digest)
  • 60Steps. 104 steps, 4 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4793 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Failures and branches. 2 branches, has a failure section
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 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
  • -32 of 16 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 325: enough signal without eating the budget
  • +4Structure: 56 headings
  • +3Step-by-step instructions: 104 items
  • +4Has examples (25 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

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