AC cord-trees
Dynamic task tree orchestration inspired by Cord protocol. Agent builds its own coordination tree at runtime — deciding decomposition, parallelism, and dependencies dynamically. Implements spawn (isolated context) vs fork (inherited context) as first-class primitives, plus ask (human elicitation) and serial (ordered sequences). Use when: complex goals that need dynamic decomposition, tasks where the agent should decide how to break down work, multi-agent coordination with runtime flexibility, human-in-the-loop checkpoints. Triggers: "figure out how to do X", "decompose this task", "build a task tree for", "dynamic orchestration", "cord-style", "self-organizing agents"
Dynamic task tree orchestration inspired by Cord protocol.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 2. 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 53/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 100Steps. 5 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2296 tokens
- low 11 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 678: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 5 items
- +4Has examples (18 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.