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BB team-loop

Autonomous task loop engine — self-verifying, goal-driven, human-out-of-loop. Triggered by: loop, team-loop, 迭代研发, 迭代. Human sets a quantifiable goal (e.g., coverage ≥ 50%), agent team autonomously executes plan→execute→verify→memorize→replan cycles with zero human intervention. Suitable for: coverage improvement, code migration, refactoring, test completion, token-budget tasks. Unlike team-dev (waterfall: spec→design→code→test), team-loop is a goal-driven loop engine that self-iterates until the target is met.

ClawHub Agent Skills author: iFeel v2.4.1 MIT-0 4 files body ≈ 8 556 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Autonomous task loop engine — self-verifying, goal-driven, human-out-of-loop.

As a process B 66/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, execution cost

AnalyzerSoftware 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
B
81/100
safety, quality, tests
Safety 60%
94
Quality 40%
61
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 6

✓ No critical or high findings

Medium and low: 6
  • low Secrets in code secret-high-entropy-token skill-cn.md:450
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "loop_id":"manu…2-L",
    quoted
  • low Secrets in code secret-high-entropy-token skill-cn.md:466
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "loop_id":"manu…2-L",
    quoted
  • low Secrets in code secret-high-entropy-token skill-cn.md:548
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    格式: `{source}-{timestamp}-{nonce}` (例: `manu…2b1`)
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:499
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "loop_id":"manu…2-L",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:515
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "loop_id":"manu…2-L",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:597
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    Format: `{source}-{timestamp}-{nonce}` (e.g., `manu…2b1`)
    detector

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8556 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "intent"

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 23 mutating operations with no state check
  • 40Execution cost. Instruction body is 8556 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Steps. 65 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 19 top-level sections: this looks like several domains in one skill

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 516: enough signal without eating the budget
  • +4Structure: 47 headings
  • +3Step-by-step instructions: 65 items
  • +4Has examples (24 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: suspicious
This is not malware, but it deliberately lets an agent autonomously edit, test, commit, revert, and persist repository context, so users should review it carefully before installing.
LLM: suspicious (high) · VirusTotal: · 9 Jul 2026