SKILLEMALL.ai

AC caid-multi-agent

Coordinate multiple sub-agents to collaboratively complete long-horizon software engineering tasks using the CAID (Centralized Asynchronous Isolated Delegation) paradigm. Use when tasks require complex multi-file edits, interdependent subtasks, parallelizable work, or when a single agent would take too long. This skill implements branch-and-merge coordination with git worktree isolation, dependency-aware task delegation, and structured integration. CRITICAL: Never use CAID as a fallback after single-agent failure; use from the outset. Max 2-4 engineers (8 absolute max). Physical git worktree isolation is mandatory; soft isolation degrades performance.

ClawHub Agent Skills author: Simon v1.2.0 MIT-0 3 files body ≈ 3 335 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationAI 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%
82
Run on models
none yet
Process rating
C
55/100
Has gaps
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

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Coordinate multiple sub-agents to collaboratively complete long-ho… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    Process rating: all ten parameters 55/100

    • 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. 18 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 85Steps. 77 steps, 1 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3335 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
    • +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 659: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 77 items
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    This is an instruction-only multi-agent coding workflow, but it includes destructive git cleanup and reset examples that users should run only after checking the target worktree.
    LLM: benign (high) · VirusTotal: · 29 May 2026