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Unified Diary System: A context-preserving automated logger for multi-project development.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 12 files body ≈ 2 253 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Unified Diary System: A context-preserving automated logger for multi-project development.

As a process B 68/100 · Nearly there — weak spots: result and completion, when it triggers

ProcedureNotionObsidianSoftware developmentAI and agentsWriting and documentstype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
74
Run on models
none yet
Process rating
B
68/100
Nearly there
When it triggers w 12
20
Result and completion w 14
40
Tools and files w 18
60
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:48
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      python {diary_system_path}/scripts/fetch_diaries.py "<Abso…ary>"
      quoted

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"

    Process rating: all ten parameters 68/100

    • 20When it triggers. No condition that starts the skill
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, web, git, python) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 43 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2253 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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)
    • +3Description length 90: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -227 emoji in the instructions: noise for the model
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (5 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.