BF chanjing-tts-voice-clone
Use Chanjing TTS API to synthesize speech from text, using user-provided voice. Primary credential: credentials.json (app_id/secret_key; access_token and expire_in persisted on disk—do not commit; user accepts file-based secrets). Same credentials file as chanjing-credentials-guard. Not OpenClaw primaryEnv. Default path in metadata.openclaw.credentialModel. CHANJING_API_BASE and CHANJING_CONFIG_DIR optional. User supplies a public URL for reference audio (fetched by Chanjing servers). No ffmpeg/ffprobe required by this skill's scripts.
As a process F 36/100 · Will not run — References files that are not bundled: scripts/*.py
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- The text references files that are not there: add them or drop the references.
- 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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:214High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"data": "C-Au…0fc"
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:255High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"id": "C-Au…0fc",
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:305High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"audio_man": "C-Au…0fc",
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:318High-entropy token-like string (may be an id, hash or a credential)| audio\_man | string | | Yes | C-Au…0fc | Voice ID, obtained from previous step |
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 541 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
missing-refreference to a missing file: scripts/*.py - note
frontmatter-keyunknown frontmatter key "binaries" - note
frontmatter-keyunknown frontmatter key "env" - note
frontmatter-keyunknown frontmatter key "sibling_skills" - note
frontmatter-keyunknown frontmatter key "credential_hint"
Process rating: all ten parameters 36/100
- 0Tools and files. 1 referenced file(s) missing: scripts/*.py
- 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
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 11 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 85Steps. 15 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3709 tokens
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -31 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 541: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.