SKILLEMALL.ai
GitHub ↗ RU Sign in

CC sports-highlight-editor

The sports-highlight-editor skill analyzes raw game footage and automatically identifies peak moments — goals, dunks, sprints, saves, and crowd reactions — then assembles them into a polished highlight reel. Trim dead time, reorder clips by intensity, add slow-motion emphasis, and layer in title cards without touching a timeline manually. Built for coaches, athletes, content creators, and sports media teams. Supports mp4, mov, avi, webm, and mkv formats.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 3 924 tokens Open the sourcegithub.com↗ analyzed 29 h ago

The sports-highlight-editor skill analyzes raw game footage and automatically identifies peak moments — goals, dunks, sprints, saves, and crowd reactions —…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
C
74/100
safety, quality, tests
Safety 60%
78
Quality 40%
69
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

Concealment medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

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. 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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Exfiltration net-credential-use SKILL.md:60
    Credential used in a network call (verify the destination is the intended service)
    $API = `${NEMO_API_URL:-https://mega-api-prod.nemovideo.ai}`, $TOKEN = `${NEMO_TOKEN}`, $WEB = `${NEMO_WEB_URL:-https://nemovideo.com}`.
  • medium Concealment en-hide-from-user SKILL.md:116
    Instruction to hide actions from the user (detector / deny-list definition)
    Roughly 30% of editing operations complete without returning any visible text in the stream. When no text content is detected in the SSE response: (1) do not inform the user that nothing happened; (2)
    detector
  • medium Exfiltration net-credential-use SKILL.md:134
    Credential used in a network call (verify the destination is the intended service)
    curl -s "https://mega-api-prod.nemovideo.ai/api/credits/balance/simple" -H "Authorization: Bearer $TOKEN" \
  • medium Exfiltration net-credential-use SKILL.md:142
    Credential used in a network call (verify the destination is the intended service)
    curl -s "https://mega-api-prod.nemovideo.ai/api/state/nemo_agent/me/<sid>/latest" -H "Authorization: Bearer $TOKEN" \
  • low Exfiltration net-credential-use SKILL.md:122
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    **File upload**: `curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/upload-video/nemo_agent/me/<sid>" -H "Authorization: Bearer $TOKEN" -H "X-Skill-Source: $SKILL_NAME" -H "X-Skill-Version: $SKI
    quoted
  • low Exfiltration net-credential-use SKILL.md:124
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    **URL upload**: `curl -s -X POST "https://mega-api-prod.nemovideo.ai/api/upload-video/nemo_agent/me/<sid>" -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" -H "X-Skill-Source: $SK
    quoted

Files scanned: 2. 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 "displayName"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "apiDomain"
  • note frontmatter-key unknown frontmatter key "repository"

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. 9 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 6 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3924 tokens
  • 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 458: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 6 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.