SKILLEMALL.ai

AC self-improving-meta

Logs agent-infrastructure issues (prompt drift, rule conflicts, hook failures, context bloat). Default is log-only. Claude/Codex project hooks fire only on explicit meta signals (self-improving-meta, prompt-governance, stackability). The optional OpenClaw agent:bootstrap hook is session-start and not matcher-gated; if enabled it injects a log-only reminder and must not edit files. Does not edit AGENTS.md, SOUL.md, TOOLS.md, MEMORY.md, hooks, or skills unless the user requests a reviewed diff in the current session. Use when prompt-file instructions are misread, a hook fails, a skill does not activate, rules conflict, prompt files waste context, memory is stale, or a skill template is incomplete.

ClawHub Agent Skills author: José I. O. v1.2.4 MIT-0 15 files · 3 scripts body ≈ 6 809 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6809 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 63/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 18 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6809 tokens
  • 100Steps. 133 steps
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 9 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 22 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 704: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 133 items
  • +4Has examples (18 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

External checks

ClawHub: clean
This skill mainly logs agent-infrastructure problems and its write-capable or hook behaviors are disclosed, opt-in, and scoped to local project files.
LLM: benign (high) · VirusTotal: · 28 Aug 2026