SKILLEMALL.ai

AF self-improvement

Capture durable lessons from debugging, user corrections, missing capabilities, and repeated workflow friction so future sessions avoid the same mistakes. Use this skill when a non-obvious failure is diagnosed, the user corrects or updates the agent, a workaround or project convention is discovered, a capability is missing, a solved issue should be promoted into shared memory, or you should review prior learnings before changing a known-problem area. Do not use for trivial typos, expected failures, straightforward retries, or one-off noise with no reusable lesson.

ClawHub Agent Skills author: Tristan Manchester v1.0.0 MIT-0 24 files · 3 scripts body ≈ 2 183 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 44/100 · Will not run — References files that are not bundled: scripts/..., references/..., assets/...

ProcedureLearningAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: scripts/..., references/..., assets/...
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/...
  • warning missing-ref reference to a missing file: references/...
  • warning missing-ref reference to a missing file: assets/...

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: scripts/..., references/..., assets/...
  • 0Tools and files. 3 referenced file(s) missing: scripts/..., references/..., assets/...
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (self-improvement) differs from the folder (actual-self-improvement)
  • 50When it triggers. No condition that starts the skill
  • 85Steps. 58 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 2183 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress

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
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 570: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 58 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (6 of 7)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

External checks

ClawHub: clean
This skill transparently creates workspace memory notes for reusable lessons, with no evidence of exfiltration or hidden destructive behavior, but users should review what gets persisted.
LLM: benign (medium) · VirusTotal: · 29 May 2026