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AC ia-agent-native-architecture

Design agent-native applications where agents replace UI users as the primary actor. Use when designing MCP tools, agent-loop architectures, system prompt design, hooks policy, shared-workspace file patterns, or self-modifying agent systems.

ClawHub Agent Skills author: Ilia Alshanetsky v4.5.2 MIT-0 27 files body ≈ 1 429 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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%
97
Quality 40%
83
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

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
    • low Secrets in code secret-high-entropy-token references/dynamic-context-injection.md:342
      High-entropy token-like string (may be an id, hash or a credential)
      USER…9e1

    A further 2 matches are quotations in this security skill's documentation and are not counted as findings.

    Files scanned: 27. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "class"

    Process rating: all ten parameters 54/100

    • 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
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (ia-agent-native-architecture) differs from the folder (compound-eng-agent-native-architecture)
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Execution cost. Instruction body is 1429 tokens
    • 100Running it twice. No mutating operations

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 241: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 22 items
    • +4Reference files are cited in the instructions (24 of 24)

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

    External checks

    ClawHub: suspicious
    This is a documentation-only architecture skill, but it includes copyable guidance for broad file, network, persistent-memory, deployment, and self-modifying-agent capabilities that should be reviewed before use.
    LLM: suspicious (high) · 8 Sept 2026