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AB sprint-contract

Multi-agent development workflow with Sprint Contracts and independent QA evaluation. Use when building features, fixing complex bugs, or any task that involves spawning sub-agents to do work. Implements the Planner-Generator-Evaluator pattern (inspired by Anthropic's GAN-style harness design) to ensure quality through explicit completion criteria and independent testing. Triggers on development tasks, feature builds, bug fixes, code reviews, or when spawning coding agents.

ClawHub Agent Skills author: Mrpixelraf v1.0.0 MIT-0 3 files body ≈ 997 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerAI and agentsSoftware developmentOperations and projectstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
55
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: 3. 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 67/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 997 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 478: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a disclosed instruction-only development workflow for using sub-agents and local handoff files, with no evidence of hidden code, credential access, or data exfiltration.
    LLM: benign (high) · VirusTotal: · 29 May 2026