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AD archon-nostr

Derive Nostr identity (npub/nsec) from Archon DID. Use when unifying DID and Nostr identities so both use the same secp256k1 key. Requires existing Archon wallet with ARCHON_PASSPHRASE set.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 396 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Derive Nostr identity (npub/nsec) from Archon DID.

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches

Proceduretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:14
      Pipe-to-shell installer from a well-known host (still executes remote code) (quoted — discussed, not commanded)
      - `nak` CLI: `curl -sSL https://raw.githubusercontent.com/fiatjaf/nak/master/install.sh | sh`
      quoted

    Files scanned: 2. 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 47/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 75Steps. 3 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 396 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

    • +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
    • +1No license
    • +2Single-language instructions
    • +3Description length 189: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.