SKILLEMALL.ai

AC self-improving-agent

Log high-signal corrections, tool failures, feature requests, and recurring workflow lessons to a lightweight .learnings/ directory, then promote only repeated or durable learnings into AGENTS.md, TOOLS.md, SOUL.md, MEMORY.md, or project instructions. Use in OpenClaw or coding-agent workspaces that should improve over time without accumulating noise.

ClawHub Agent Skills author: AmitPNYC v1.0.0 MIT-0 2 files body ≈ 1 398 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 40Consistency. Frontmatter name (self-improving-agent) differs from the folder (amitpnyc-self-improving-agent)
    • 65Failures and branches. 3 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 43 steps
    • 100Execution cost. Instruction body is 1398 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 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

    • +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 352: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This instruction-only skill keeps local project learning notes and clearly limits higher-authority memory or instruction edits to user-authorized situations.
    LLM: benign (high) · VirusTotal: · 29 May 2026