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CD gtm-super-intelligence

Sales, GTM & Marketing Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

coco-research/coco Agent Skills author: coco-research 44 files · 8 scripts body ≈ 548 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Sales, GTM & Marketing Super Intelligence Team.

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentAI and agentsMarketingtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
99
Quality 40%
62
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-background-process personas/aaron-ross.md:114
    Starts a background / autostarted process
    What is striking in his recent thinking is the willingness to disown the popular reading of his own work. By 2025 he was openly saying the market misunderstood Predictable Revenue — that people "bolt

Files scanned: 44. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "team_id"
  • note frontmatter-key unknown frontmatter key "team_name"
  • note frontmatter-key unknown frontmatter key "last_updated"
  • note frontmatter-key unknown frontmatter key "schema_version"

Process rating: all ten parameters 41/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (gtm-super-intelligence) differs from the folder (gtm)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 4 steps
  • 100Execution cost. Instruction body is 548 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (5 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)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -36 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 184: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 4 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.