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AC lobster-dev

Develop, extend, and contribute to Lobster AI — the multi-agent self-extending bioinformatics engine. Use when working on Lobster codebase, creating agents/services, understanding architecture, fixing bugs, adding features, or contributing to the open-source project. IMPORTANT: Before creating new agents or packages, follow the planning workflow first (see "What To Do Based On Your Task" → planning-workflow.md). Trigger phrases: "add agent", "create service", "extend lobster", "contribute", "understand architecture", "how does X work in lobster", "fix bug", "add feature", "write tests", "lobster development", "agent development", "bioinformatics code", "build a new agent for", "add support for", "create plugin", "new domain"

ClawHub Agent Skills author: cewinharhar v1.1.402 15 files body ≈ 1 670 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 51/100 · 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
51/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 15. 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 51/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (lobster-dev) differs from the folder (lobsterbio-dev)
    • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 100Steps. 14 steps
    • 100Execution cost. Instruction body is 1670 tokens
    • 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

    • +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
    • +5Description quotes 17 example trigger phrases
    • +3Description length 736: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (10 of 12)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: clean
    This is a Lobster AI development guide with expected setup, credential, and session-state cautions, but no evidence of hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026