SKILLEMALL.ai

AC next

Suggest next actions after completing any task. Auto-invocation via Stop hook + UserPromptSubmit reactive backstop, owned by the `next-invocation-guard` plugin (local-only, ported from `resources/next-trigger.sh` + `resources/next-reactive-guard.sh`). Fires when assistant response contains completion keywords (locale patterns in `data/*.regex`). stall-detect - detect stalled follow-up steps and invoke /fix [stall-detect.md], ask-gates - recording-skip / decision-deferral forced-ask / TaskList primary-source / current-work confirmation gates [ask-gates.md], suggestion-patterns - per-context "After X" next-action option templates [suggestion-patterns.md]. Use when "next action", "what next", "stall", "stuck", "not progressing", "follow-up missing" is mentioned.

ClawHub Agent Skills author: es6kr v0.10.0 MIT-0 9 files · 2 scripts body ≈ 6 147 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6147 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "depends-on"

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 6147 tokens
  • 100Steps. 27 steps
  • 100Failures and branches. 11 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -5TODO / placeholder text left in the skill
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 769: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
The skill's next-action purpose is understandable, but it uses persistent global agent hooks, broad transcript/workspace inspection, automatic follow-up skill invocation, and local debug logging in ways users should review carefully before installing.
LLM: suspicious (high) · 6 Sept 2026