SKILLEMALL.ai

AB taku-reflect

User-invoked reflection. Three modes: Learn (script-backed recording, searching, pruning, exporting, and optional bootstrap for user-approved patterns, pitfalls, preferences, discoveries), Retro (weekly engineering retrospective with git commit analysis, metrics, team breakdowns, trends), and Write Skill (codify recurring learnings into reusable skills). Triggers on "what have we learned", "add learning", "show learnings", "weekly retro", "what did we ship", "engineering retrospective", "write a skill", "create a skill", "codify this pattern", "总结一下", "学到了什么", "记录一下", "回顾这周", "做个retro", "写个技能", "把这个模式固化". Also invoke when the user expresses satisfaction or frustration after completing work — these are natural reflection moments.

ClawHub Agent Skills author: KennyWu v1.0.0 MIT-0 6 files body ≈ 3 150 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 78/100 · Nearly there — weak spots: inputs and preconditions

AnalyzerOperations and projectsSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
95
Quality 40%
99
Run on models
none yet
Process rating
B
78/100
Nearly there
Inputs and preconditions w 11
0
Result and completion w 14
60
When it triggers w 12
70
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash Read Write Edit Glob Grep

    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 78/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 51 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Failures and branches. 6 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3150 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +3Description length 738: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 51 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.

    External checks

    ClawHub: clean
    This skill is a disclosed local reflection tool that can save project learnings and retrospectives, with no evidence of hidden exfiltration or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026