SKILLEMALL.ai

AB project-memory-workflow

Maintain AI-readable project memory for software repositories. Use when onboarding to a codebase, initializing project context, detecting missing project guidance or progress documents, preserving repository conventions and user changes, or recording implementation, verification, decisions, and risks. Trigger on requests such as "初始化项目", "初始化项目记忆", "init", "init project", "set up project memory", and "建立项目记忆". Support OpenAI/Codex AGENTS.md and Claude CLAUDE.md conventions.

ClawHub Agent Skills author: _YLong v0.1.0 MIT-0 6 files body ≈ 1 190 tokens Open the sourceclawhub.ai analyzed 2 d ago

Maintain AI-readable project memory for software repositories.

As a process B 75/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 75/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 9 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1190 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 478: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 27 items
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill is a disclosed repository documentation workflow that can create or update project-memory files, with some activation ambiguity users should be aware of.
    LLM: benign (high) · VirusTotal: · 14 Aug 2026