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BC ov-session-report

Analyze local Q&A session JSONL files and produce a complete, auditable English Markdown weekly report over consecutive time windows, with all data required for downstream Feishu document and whiteboard rendering. Use for requests such as "analyze the latest week of sessions," "generate a community Q&A weekly report," or "compare with last week." This skill does not ingest other message sources or create/upload online documents.

volcengine/OpenViking Agent Skills author: volcengine AGPL-3.0 1 file body ≈ 7 510 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Analyze local Q&A session JSONL files and produce a complete, auditable English Markdown weekly report over consecutive time windows, with all data required…

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

AnalyzerData and analyticstype 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
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
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

  1. 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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7510 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 12 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 7510 tokens
  • 100Steps. 170 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 20 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

  • +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
  • +5Description quotes 3 example trigger phrases
  • +3Description length 432: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 170 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.