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BC loki-mode-openclaw

Launch Loki Mode autonomous SDLC agent. Handles PRD-to-deployment with minimal human intervention. Invoke for multi-phase development tasks, bug fixing campaigns, or full product builds.

The skillemall take

The skill promises to launch an autonomous agent for the full development cycle—from requirements to deployment with minimal human input. Eight modules and six scripts in the files, low token count (898), no critical errors. Quality score is 81, but process score dropped to 54, suggesting the logic works while reproducibility needs work.

No real test runs available: sandbox untouched, models untested. The skill claims support for many platforms (Claude, Cursor, DeepSeek, etc.), but that's just manifest entries. Install if you need a template for multi-phase tasks and can debug workflows. Expect the first run to need tuning.

asklokesh/claudeskill-loki-mode Agent Skills author: asklokesh 8 files · 6 scripts body ≈ 898 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Launch Loki Mode autonomous SDLC agent.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureSoftware developmentData and analyticsAI and agentstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
54/100
Has gaps
Failures and branches w 10
0
When it triggers w 12
20
Running it twice w 4
30
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: 8. 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 54/100

    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (loki-mode-openclaw) differs from the folder (openclaw)
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 42 steps
    • 100Execution cost. Instruction body is 898 tokens
    • 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)
    • +3Output format is not stated: the model decides each time
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 186: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 42 items
    • +4Has examples (3 code blocks)

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