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BC Operational Framework

A disciplined, reproducible workflow for AI agents to log decisions, create rollback snapshots, and generate briefings for any change or feature implementation.

ClawHub Agent Skills author: Tim v1.0.0 MIT-0 2 files body ≈ 1 225 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

ProcedureInfrastructureAI 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
62/100
Has gaps
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
40
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 62/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (Operational Framework) differs from the folder (operational-framework)
    • 60Tools and files. Uses tools (bash, git) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 33 steps, 1 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 1225 tokens
    • 100Progress reporting. Reports progress
    • low 11 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 160: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 33 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

    External checks

    ClawHub: clean
    The skill appears to perform disclosed local snapshot/restore behavior, with an overwrite-risk caveat rather than evidence of deception or malware.
    LLM: benign (medium) · VirusTotal: · 29 May 2026