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AC veille

RSS feed aggregator, deduplication engine, LLM scoring, and output dispatcher for OpenClaw agents. Use when: fetching recent articles from configured sources, filtering already-seen URLs, deduplicating by topic, scoring with LLM, dispatching digests to Telegram/email/Nextcloud/file. Enhanced by mail-client (email output) and nextcloud-files (cloud storage).

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 3 312 tokens Open the sourcegithub.com↗ analyzed 33 h ago

RSS feed aggregator, deduplication engine, LLM scoring, and output dispatcher for OpenClaw agents.

As a process C 62/100 · Has gaps — weak spots: when it triggers, running it twice, progress reporting

ProcedureTelegramAI 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%
94
Quality 40%
86
Run on models
none yet
Process rating
C
62/100
Has gaps
Progress reporting w 2
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

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

    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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • medium Dangerous commands cmd-autorun-instruction references/troubleshooting.md:57
      Instructs the agent to auto-run a script on every session
      **Fix:** Always run `veille.py` with its full path, or from the `scripts/` directory:
    • low Exfiltration exfil-webhook-url scripts/dispatch.py:493
      Webhook / callback URL commonly used for exfiltration (verify the destination) (the skill's own vendor host; quoted — discussed, not commanded)
      f"https://api.telegram.org/bot{token}/sendMessage",
      vendor-hostquoted

    Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "ontology"

    Process rating: all ten parameters 62/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 9 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 50 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3312 tokens
    • 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)
    • -34 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 359: enough signal without eating the budget
    • +4Structure: 38 headings
    • +3Step-by-step instructions: 50 items
    • +3Output format is stated explicitly
    • +4Has examples (25 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.