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BD bridge-poll

Agent-to-agent communication via HTTP bridge with cron-based polling. Use when: (1) two OpenClaw agents need to talk across instances, containers, or machines, (2) setting up a bridge relay between agents, (3) debugging silent/unresponsive bridge connections, (4) an agent needs to poll another agent's messages and respond autonomously. Works on any platform — VPS, Docker, bare metal, cloud. Requires ACP_BRIDGE_TOKEN env var (shared secret for bridge auth). Helper scripts require BRIDGE_TOKEN and BRIDGE_URL env vars. No external network calls — all communication stays between the two configured agents via the bridge server.

ClawHub Agent Skills author: Joel Yi - DeployAIBots.com v1.0.1 MIT-0 9 files · 1 script body ≈ 1 273 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureDockerAI 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
B
89/100
safety, quality, tests
Safety 60%
93
Quality 40%
83
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Exfiltration net-credential-use SKILL.md:28
      Credential used in a network call (verify the destination is the intended service)
      curl -s http://loca…790/api/health -H "Authorization: Bearer $ACP_BRIDGE_TOKEN"
    • low Dangerous commands cmd-background-process references/gotchas.md:177
      Starts a background / autostarted process
      nohup python3 /path/to/acp_bridge.py > /tmp/acp_bridge.log 2>&1 &
    • low Dangerous commands cmd-background-process SKILL.md:26
      Starts a background / autostarted process
      nohup python3 acp_bridge.py > /tmp/acp_bridge.log 2>&1 &

    Files scanned: 9. 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 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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. 8 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1273 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (3 tags): a typed call is more reliable

    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
    • -2localhost URLs: will not work for another user
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 630: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This is a coherent agent-to-agent bridge, but it needs Review because it creates persistent autonomous communication and includes under-documented destructive and broad workspace-control behavior.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026