SKILLEMALL.ai

BC loop-builder

Use this when the user wants you to BUILD a self-running agent loop — take a recurring chore and turn it into something that fires on its own (a schedule or event), does the work, checks the result against an OBJECTIVE pass/fail gate, records state to disk, and repeats until a real stopping condition — so it keeps working after they stop prompting. The tell is a judged outcome plus a trigger, not just a timer. Trigger on: "build me a loop / set up a loop", "automate this whole flow so it just runs every few hours", "turn my weekly/standup chore into something self-running", "keep fixing/triaging X and re-running until it's green, then stop", "check Y for me unattended", "every morning read yesterday's failures and write them up", "add the checker half that grades what my bot produces", or any background/recurring task they want to hand off. Covers CI triage, PR review/merge-checking, digests, lint/build loops, dependency bumps, doc refresh. Delivers the whole system — trigger, state file, procedure skill, hard-stop gate, command allowlist, supervised rollout. Skip for: one-off tasks done now, writing literal loop code (a Python while/for-loop or an infinite-render bug), and plain scheduling with no work-or-gate (a vercel.json cron, a GitHub Actions YAML).

ClawHub Agent Skills author: Hansraj Singh Thakur v0.2.0 MIT-0 2 files body ≈ 2 393 tokens Open the sourceclawhub.ai analyzed 2 d ago

Use this when the user wants you to BUILD a self-running agent loop — take a recurring chore and turn it into something that fires on its own (a schedule or…

As a process C 62/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

GeneratorGitHubSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1276 chars, limit 1024

Process rating: all ten parameters 62/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 13 mutating operations with no state check
  • 40Consistency. Frontmatter name (loop-builder) differs from the folder (loop-creator)
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web, git, node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 31 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Execution cost. Instruction body is 2393 tokens
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (4 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)
  • +3Description length 1275: 120–800 characters recommended
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 31 items
  • +3Output format is stated explicitly

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: clean
This skill is a disclosed guide for building supervised self-running agent loops with state, gates, cost checks, and command guardrails.
LLM: benign (high) · VirusTotal: · 16 Jun 2026