SKILLEMALL.ai

BC system-audit

Periodic evidence-based self-audit of the whole agent system — the orchestrator, its docs/SSOTs, rules, memory, subagents, tools, infra and the work it claims to have delivered — followed by immediate cheap-safe fixes and a ranked backlog. Use when the user says 'аудит системы', 'проверь себя', 'самопроверка', 'system audit', 'audit yourself', 'health check', 'что у нас накопилось', 'система раздулась', 'проверь что работает а что на бумаге', 'gap-анализ', 'что удалить', or on cadence triggers (every ~10 sessions, before a milestone, after a big refactor, when memory hits its caps, when onboarding this repo to a new agent). Seven lenses: delivery reality · knowledge drift · operational layer · layer telemetry (did it ever fire) · tools & infra · domain gaps · anti-bloat subtraction. Not for reviewing a single diff (use a diff-review pass) and not for probing a running product (use /memory-kit:qa-sweep).

awrshift/agent-memory-kit Agent Skills author: awrshift 7 files · 4 scripts body ≈ 2 631 tokens Open the sourcegithub.com↗ analyzed 14 h ago

Periodic evidence-based self-audit of the whole agent system — the orchestrator, its docs/SSOTs, rules, memory, subagents, tools, infra and the work it claims…

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

AnalyzerAI and agentsSoftware developmentOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 18): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    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
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 13 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 26 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2631 tokens
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Description length 916: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.