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AC aegis-bridge

Orchestrate Claude Code sessions via Aegis HTTP/MCP bridge. Use when spawning CC sessions for coding tasks, implementing issues, reviewing PRs, fixing CI, batch tasks, or any multi-agent workflow. Triggers on "aegis", "spawn session", "orchestrate CC", "parallel agents", "create CC session", "send to CC". Requires Aegis server running on localhost:9100.

ClawHub Agent Skills author: Emanuele v0.6.7 MIT-0 9 files · 2 scripts body ≈ 1 836 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, consistency, running it twice

AnalyzerAI and agentsSoftware developmenttype 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: aegis-bridge (ClawHub)

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 · 0

    ✓ No critical or high findings

    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 55/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 24 mutating operations with no state check
    • 40Consistency. Frontmatter name (aegis-bridge) differs from the folder (onestep-aegis)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 12 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 1836 tokens

    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

    • +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
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 355: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (3 of 4)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    The skill is a real local agent-orchestration helper, but it gives delegated coding sessions broad authority and includes examples that automatically approve security prompts.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026