BC consensus-persona-respawn
Ledger-informed persona lifecycle management. Replaces low-performing personas with successor personas derived from mistake patterns in board decision history, preserving adaptive governance over long-running automation. Reputation updates are computed by consensus-persona-engine.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:22High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…Q92+TGzG…QbU+MF6v…A1M/x9f0…ddw==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:37High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…7pY+zoMV…h0x/Ptw8…8dg==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:53High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…b00+Gxjx…zRc/oZwU…hzA==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:117High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha512-vHk/hA7/1Ack…wbo+jaSh…Gtl+A5zq…HFg==",
detector -
low Secrets in code
secret-high-entropy-tokenpackage-lock.json:149High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)"integrity": "sha5…Dsc+j03S…0oA==",
detector
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "upstream" - note
frontmatter-keyunknown frontmatter key "requires"
Process rating: all ten parameters 53/100
- 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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 548 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
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 281: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.