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BB campaign-orchestrator

Orchestrate a campaign lifecycle: briefs, budget models, UTMs, post-mortems. "set up UTM naming conventions"

indranilbanerjee/digital-marketing-pro Agent Skills author: indranilbanerjee 8 files body ≈ 3 625 tokens Open the sourcegithub.com↗ analyzed 13 h ago

Orchestrate a campaign lifecycle: briefs, budget models, UTMs, post-mortems.

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ProcedureMarketingtype 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
B
90/100
safety, quality, tests
Safety 60%
97
Quality 40%
80
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Tools and files w 18
60
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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Secrets in code secret-high-entropy-token campaign-planning.md:101
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - `2026…-v2`
      quoted
    • low Secrets in code secret-high-entropy-token campaign-planning.md:102
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - `2026…ool`
      quoted
    • low Secrets in code secret-high-entropy-token campaign-planning.md:103
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      - `2026…nts`
      quoted

    Files scanned: 8. 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 65/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 60Steps. 106 steps, 5 vague phrases
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3625 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 108: 120–800 characters recommended
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 106 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.