SKILLEMALL.ai

AB deep-planner

A meta-skill that activates before complex tasks to enforce structured planning, step-by-step execution, and self-reflection. Works like Claude Code's TodoList — generates a visible task plan, persists it to `.todolist/`, checks off steps as they complete, and pauses at critical decision points for user confirmation. Trigger this skill whenever: - The request involves 3+ steps or multiple tools/skills chained together - Keywords like "research", "analyze", "plan", "design", "compare", "write a report" appear - Key variables are undefined (audience, platform, format, scope, constraints) - The domain is unfamiliar or information may be time-sensitive - A task requires coordinating more than one other skill This skill does NOT produce the final output — it plans and supervises execution, then delegates to the appropriate domain skills (e.g. web search, code execution, platform-specific skills). Think of it as the project manager, not the worker.

ClawHub Agent Skills author: Jzw6 v1.0.2 MIT-0 3 files body ≈ 1 455 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerPersonal productivitySoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 3. 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 66/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Failures and branches. 2 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 16 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1455 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 958: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This is a planning-only skill that clearly discloses local todo-file persistence and does not include executable code, network calls, credential handling, or hidden behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026