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BF reelclaw

Create, produce, and schedule UGC-style short-form video reels at scale. Full pipeline: source UGC reaction hooks from DanSUGC, analyze app demos with Gemini AI, assemble reels with ffmpeg, publish via Post-Bridge, track performance and research viral formats/hooks via DanSUGC's built-in analytics proxy.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 5 212 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Create, produce, and schedule UGC-style short-form video reels at scale.

As a process F 36/100 · Will not run — References files that are not bundled: references/virality.md

GeneratorMarketingMedia and videotype 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
B
80/100
safety, quality, tests
Safety 60%
94
Quality 40%
59
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: references/virality.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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
  • low Exfiltration exfil-secret-in-url references/tools-setup.md:138
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (destination is a well-known publishing service; quoted — discussed, not commanded)
    "https://generativelanguage.googleapis.com/upload/v1beta/files?key=…" \
    known servicequoted
  • low Exfiltration exfil-secret-in-url references/tools-setup.md:145
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (destination is a well-known publishing service; quoted — discussed, not commanded)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/gemi…ent?key=…" \
    known servicequoted
  • low Exfiltration net-credential-use references/tools-setup.md:145
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/gemi…ent?key=…" \
    known service
  • low Exfiltration exfil-secret-in-url SKILL.md:200
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (destination is a well-known publishing service; quoted — discussed, not commanded)
    "https://generativelanguage.googleapis.com/upload/v1beta/files?key=…" \
    known servicequoted
  • low Exfiltration exfil-secret-in-url SKILL.md:206
    Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (destination is a well-known publishing service; quoted — discussed, not commanded)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/gemi…ent?key=…" \
    known servicequoted
  • low Exfiltration net-credential-use SKILL.md:206
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)
    curl -s "https://generativelanguage.googleapis.com/v1beta/models/gemi…ent?key=…" \
    known service

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 ≈ 5212 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/virality.md
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: references/virality.md
  • 0Tools and files. 1 referenced file(s) missing: references/virality.md
  • 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. 8 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 5212 tokens
  • 100Steps. 67 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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

  • +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 305: enough signal without eating the budget
  • +4Structure: 44 headings
  • +3Step-by-step instructions: 67 items
  • +4Has examples (30 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.