SKILLEMALL.ai

BC agent-loop-engineering

Execute an authorized software goal through low-context, bounded-autonomous AI coding loops with persistent state, proactive repair, automatic and functional evidence, layered stage review, independent final acceptance, safe workspace boundaries, and resumable handoffs. Use when a target and acceptance criteria are clear and the user asks to implement, debug, verify, continue autonomously, follow Controller-Developer-QC cycles, resume after context loss, or reduce repeated context and documentation. Typical triggers include keep going, continue where we left off, run it autonomously and fix what breaks, 接着跑 / 继续做 / 自动修 / 断点续跑 / 别再问我每一步, we are stuck on the same failure, 卡在同一个错误上, resume after compaction, 上下文快满了, stop burning tokens on repeated context, run the regression after this repair, and make a flaky test deterministic. Also use for bounded-autopilot, single-writer loop state, failure-signature stop rules, delivery-class-aware evidence, and anti-doc-bloat execution. For vague goals, legacy-state conflicts, requirement discovery, target rebaseline, scope creep triage, or QA acceptance authority, use cms-project-governance first.

ClawHub Agent Skills author: EnglandTong v2.2.0 MIT-0 27 files body ≈ 3 470 tokens Open the sourceclawhub.ai analyzed 35 h ago

Execute an authorized software goal through low-context, bounded-autonomous AI coding loops with persistent state, proactive repair, automatic and functional…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
53
Run on models
none yet
Process rating
C
51/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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: 27. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1151 chars, limit 1024

Process rating: all ten parameters 51/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 53 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3470 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 14 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)
  • +3Description length 1151: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: clean
The skill is a disclosed project-local coding loop helper with bounded autonomy and no evidence of hidden exfiltration, unsafe persistence, or deceptive behavior.
LLM: benign (high) · VirusTotal: · 7 Sept 2026