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CD aeon-update

Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.

aeonfun/aeon Agent Skills author: aeonfun 1 file body ≈ 5 351 tokens Open the sourcegithub.com↗ analyzed 67 min ago

Pull framework updates from the upstream Aeon repo into this instance - 3-way merges canon's new commits into a PR, never clobbering operator config.

As a process D 43/100 · Unfinished process — References files that are not bundled: scripts/aeon_update.py, scripts/**, scripts/validate-skill-packs.mjs

AnalyzerGitHubData and analyticsFinancetype 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
C
79/100
safety, quality, tests
Safety 60%
99
Quality 40%
50
Run on models
none yet
Process rating
D
43/100
Unfinished process
References files that are not bundled: scripts/aeon_update.py, scripts/**, scripts/validate-skill-packs.mjs
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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 Exfiltration net-credential-use SKILL.md:258
    Credential used in a network call (verify the destination is the intended service) (negated — the text forbids it)
    Every network call is `gh api`, which authenticates via `GITHUB_TOKEN` automatically - no `curl`, no `./secretcurl`, no `$SECRET` on the command line for the Bash permission layer to refuse, and no se
    negated

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5351 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/aeon_update.py
  • warning missing-ref reference to a missing file: scripts/**
  • warning missing-ref reference to a missing file: scripts/validate-skill-packs.mjs
  • warning missing-ref reference to a missing file: scripts/tests/test_validate_skill_packs.sh
  • warning missing-ref reference to a missing file: scripts/notify.sh
  • warning missing-ref reference to a missing file: scripts/foo.sh

Process rating: all ten parameters 43/100

Will not run. References files that are not bundled: scripts/aeon_update.py, scripts/**, scripts/validate-skill-packs.mjs
  • 0Tools and files. 6 referenced file(s) missing: scripts/aeon_update.py, scripts/**, scripts/validate-skill-packs.mjs
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 37 mutating operations with no state check
  • 70Execution cost. Instruction body is 5351 tokens
  • 100Steps. 33 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (15 tags): a typed call is more reliable

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 149: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.