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CD briefed

Set up and run a personal AI newsletter intelligence system called Briefed. Fetches Gmail newsletters daily, uses Claude Haiku to extract article summaries, and serves a polished local web reader app with voting, notes, and interest tracking. Use when a user asks to set up a newsletter reader, daily digest, inbox intelligence tool, or newsletter summariser with OpenClaw.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 13 files body ≈ 1 811 tokens Open the sourcegithub.com↗ analyzed 34 h ago

Set up and run a personal AI newsletter intelligence system called Briefed.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

IntegrationGmailWriting and documentstype 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
C
70/100
safety, quality, tests
Safety 60%
59
Quality 40%
87
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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.
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 · 7

  • high Dangerous commands cmd-persistence SKILL.md:128
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    # Persistent — create ~/Library/LaunchAgents/ai.openclaw.briefed.plist
  • high Dangerous commands cmd-persistence SKILL.md:155
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load ~/Library/LaunchAgents/ai.openclaw.briefed.plist
Medium and low: 5
  • low Secrets in code secret-high-entropy-token assets/reader/package-lock.json:33
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…FFX/+gVeY…NlM++NqRc…bqg==",
    detector
  • low Secrets in code secret-high-entropy-token assets/reader/package-lock.json:194
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…FrF+LTRo…W3g==",
    detector
  • low Secrets in code secret-high-entropy-token assets/reader/package-lock.json:203
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
    detector
  • low Secrets in code secret-high-entropy-token assets/reader/package-lock.json:212
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…5bm+c2gQ…aG5+esrLODihIorn+Pe6F…dXA==",
    detector
  • low Secrets in code secret-high-entropy-token assets/reader/package-lock.json:391
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…I9y+CyS8…UMQ==",
    detector

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

✓ No remarks against the Agent Skills spec

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 19 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1811 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

  • +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 373: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (12 code blocks)
  • +3All 2 scripts are documented

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