SKILLEMALL.ai

AC recursive-swarm

Bounded recursive orchestration for complex tasks that are too large for one agent turn but cleanly decompose into a few independent subproblems. Use for multi-angle research, audits, mixed research+synthesis, or coding projects that benefit from explicit planning, task-tree state, artifact folders, and controlled parallel execution. Keep recursion tight: default max depth 2, hard cap 3, preferred fan-out 2-4, and modest concurrency. Use git worktrees only for coding leaves inside git repos. Do not use for simple one-shot tasks, destructive workflows, or open-ended exploration.

ClawHub Agent Skills author: Pedro Gonzalez v0.1.1 13 files body ≈ 1 843 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
52/100
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

    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: 13. 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 52/100

    • 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. 11 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 85Steps. 104 steps, 3 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1843 tokens
    • 100Progress reporting. Reports progress
    • low 10 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
    • +3Output format is not stated: the model decides each time
    • -31 of 8 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 584: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 104 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a local orchestration helper that creates task-run files and coordinates bounded child work, with no evidence of hidden exfiltration or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026