SKILLEMALL.ai

AC todo_list

Track local todo or work-report items in a SQLite database, including planned work, progress amounts, completion status, deletion, and archiving. Use when Codex needs to add tasks, delete tasks, update progress, list current work, mark work complete, summarize progress, or archive finished or cancelled items for a user.

ClawHub Agent Skills author: Xinhai Zou v1.0.0 MIT-0 6 files body ≈ 853 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedurePersonal productivityInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 56/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 9 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (todo_list) differs from the folder (todo-list-pro)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 29 steps
    • 100Execution cost. Instruction body is 853 tokens

    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
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 321: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 29 items
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

    External checks

    ClawHub: clean
    This skill is a local todo tracker that stores and manages task data in a disclosed SQLite database without hidden network, credential, or unrelated behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026