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BF liberating-structures

Helps facilitators, leaders, and teams choose and apply the right Liberating Structures (33 microstructures) based on context like group size, available time, purpose, and facilitator experience. Includes high-quality structured knowledge and selection guidance for all 33 methods.

ClawHub Agent Skills author: AlexHT Hung v0.4.0 MIT-0 37 files body ≈ 1 963 tokens Open the sourceclawhub.ai↗ analyzed 26 h ago

Helps facilitators, leaders, and teams choose and apply the right Liberating Structures (33 microstructures) based on context like group size, available time…

As a process F 46/100 · Will not run — References files that are not bundled: scripts/ls_crawler.py, scripts/ls_structurizer.py, scripts/ls_recommender.py

ProcedureAI and agentstype 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
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
F
46/100
Will not run
References files that are not bundled: scripts/ls_crawler.py, scripts/ls_structurizer.py, scripts/ls_recommender.py
Tools and files w 18
0
Failures and branches w 10
0
Progress reporting w 2
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 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 · 0

✓ No critical or high findings

Files scanned: 37. 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 missing-ref reference to a missing file: scripts/ls_crawler.py
  • warning missing-ref reference to a missing file: scripts/ls_structurizer.py
  • warning missing-ref reference to a missing file: scripts/ls_recommender.py
  • note frontmatter-key unknown frontmatter key "emoji"
  • note edit-residue the text marks something as outdated (lines 33): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 46/100

Will not run. References files that are not bundled: scripts/ls_crawler.py, scripts/ls_structurizer.py, scripts/ls_recommender.py
  • 0Tools and files. 3 referenced file(s) missing: scripts/ls_crawler.py, scripts/ls_structurizer.py, scripts/ls_recommender.py
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 56 steps, 3 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1963 tokens

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)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 281: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 56 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (1 of 2)

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

External checks

ClawHub: clean
This is a knowledge-only facilitation skill with no executable code or system access, though users should apply consent and privacy judgment when using its recording-related workshop advice.
LLM: benign (high) · VirusTotal: · 28 May 2026