SKILLEMALL.ai

DC subagent-dag-orchestration

Use when delegated work breaks into several tasks that agents on the roster can carry out. The host's role decides what to delegate; this skill decides how to group that work. A DAG node dispatches to a roster agent, including the generic agent, and cannot call the host's tools. Choose from the tool's `subagent` parameter enum -- agent names on ordinary turns, worker labels when a Worker Table is bound. The roster describes underlying capabilities. Reading a brief or checking task status is host coordination, not a reason to create graph nodes. Before dispatching tasks one at a time, test three things: are two or more tasks independent (they can run at once), does a task hand its result to the next (a graph wires that handoff with no turn of yours in between), do the tasks want different specialists (the roster closes the tool list). If any of the three holds, orchestrate the whole task as one run_subagent_dag call. A step whose result decides what runs next is a decision node: give it routes, and guard what follows with when, all in one graph. Iteration is graphs in series, one graph per round, driven by you.

EverMind-AI/Raven Agent Skills author: EverMind-AI Apache-2.0 1 file body ≈ 10 571 tokens Open the sourcegithub.com↗ analyzed 2 d ago

The host's role decides what to delegate; this skill decides how to group that work.

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, execution cost

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
D
74/100
safety, quality, tests
Safety 60%
100
Quality 40%
36
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Execution cost w 6
40
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1127 chars, limit 1024
  • 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: Use when delegated work breaks into several tasks that agents on t… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning body-long SKILL.md body ≈ 10571 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 10571 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 40 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (24 tags): a typed call is more reliable

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)
  • +3Description length 1127: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 36.