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Generate narrative blog posts from AI coding session transcripts. Reads session files, selects sessions relevant to a topic, and produces an agent-narrated blog post about the human-agent collaboration. Supports builder's log, tutorial, and technical deep-dive styles.

modbender/skill-library-mcp Agent Skills author: modbender MIT 23 files · 11 scripts body ≈ 2 199 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate narrative blog posts from AI coding session transcripts.

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware 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
B
87/100
safety, quality, tests
Safety 60%
94
Quality 40%
76
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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.
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 Secrets in code secret-high-entropy-token references/platforms/opencode/opencode.md:49
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "ses_…BPq",
    quoted
  • low Secrets in code secret-high-entropy-token references/platforms/opencode/opencode.md:85
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "msg_…mgC",
    quoted
  • low Secrets in code secret-high-entropy-token references/platforms/opencode/opencode.md:86
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "sessionID": "ses_…BPq",
    quoted
  • low Secrets in code secret-high-entropy-token references/platforms/opencode/opencode.md:98
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "id": "msg_…Uy1",
    quoted
  • low Secrets in code secret-high-entropy-token references/platforms/opencode/opencode.md:99
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "sessionID": "ses_…BPq",
    quoted
  • low Secrets in code secret-high-entropy-token references/platforms/opencode/read-session.sh:19
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    echo "  session-id: e.g., ses_…BPq" >&2
    quoted

Files scanned: 23. 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")

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 16 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 50 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2199 tokens
  • 100Progress reporting. Reports progress
  • high The skill tells the model to perform an irreversible action with no human approval

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 268: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (0 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.