SKILLEMALL.ai
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DC start-2-3

Module 2.3: Product Strategy. Teaches strategic choices, competitive research, devil’s advocate, and executive strategy artifacts.

The skillemall take

Promises to teach strategic thinking: market positioning, competitive analysis, devil's advocate method, and presentation artifacts. Sounds like a course for product managers wanting to look serious.

Quality checks landed at 60 points. High-severity concealment issue found in the files—a red flag for a tool that should be transparent. One additional medium or low severity finding. Safety score is 81, but that doesn't offset the hidden content.

Don't install. The skill claims to educate but hides part of its material. For an analyst who needs honest tool behavior, that's a dealbreaker.

Not recommendedcritical or high security findings · low grade D
carlvellotti/claude-code-pm-course Agent Skills author: carlvellotti 5 files body ≈ 7 922 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Module 2.3: Product Strategy. Teaches strategic choices, competitive research, devil’s advocate, and executive strategy artifacts.

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerNotionAsanaLearningSoftware developmentMarketingtype 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
D
73/100
safety, quality, tests
Safety 60%
81
Quality 40%
60
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Result and completion w 14
40
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. 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 · 2

  • high Concealment en-hide-from-user SKILL.md:9
    Instruction to hide actions from the user
    At the start of this lesson, silently copy the bundled lesson assets into the workspace if they are not already present. Do not show command output to the student.
Medium and low: 1
  • low Risky intent intent-offensive-security assets/methods/devils-advocate-strategy.md:5
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (quoted — discussed, not commanded)
    Devil's advocate is a **critical thinking technique** where you deliberately challenge your own strategy to find holes, weaknesses, and faulty assumptions. It's the strategic equivalent of "red team"
    quoted

Files scanned: 5. 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")
  • warning body-long SKILL.md body ≈ 7922 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 60/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7922 tokens
  • 85Steps. 124 steps, 1 vague phrases
  • 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

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
  • -230 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 130: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 124 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.