SKILLEMALL.ai

AC debugging-network-issues

Investigates network, streaming, and protocol failures with falsification-first layered experiments. Use for ECONNRESET, HTTP/2 RST_STREAM, SSE or long-polling stalls, fixed-time drops, CDN/proxy/CGNAT timeouts, client-side proxy/TUN misrouting, CNAME-rule surprises, certificate errors, generic throughput collapse where everything returns 200 but transfers crawl, or LAN identity/topology mysteries. Trigger phrases include "socket closed unexpectedly", "stream interrupted", "fails after N seconds", "works sometimes but not always", "upstream silent for X seconds", ERR_CONNECTION_CLOSED, SSL_ERROR_SYSCALL, UNKNOWN_CERTIFICATE_VERIFICATION_ERROR, and wrong-site certificate. Treat this as the general method and unknown-root-cause fallback. Use tunnel-doctor for known Tailscale/TUN/DNS/route/WSL-Docker conflicts and for a confirmed proxy exit/node/chain quality problem.

daymade/claude-code-skills Agent Skills author: daymade 13 files · 1 script body ≈ 13 576 tokens Open the sourcegithub.com analyzed 2 h ago

Investigates network, streaming, and protocol failures with falsification-first layered experiments.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, execution cost

ProcedureDockerCloudflareSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Execution cost w 6
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • 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: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 13576 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Execution cost. Instruction body is 13576 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web, git, node) that frontmatter does not declare
  • 85Steps. 112 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 10 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 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 (32 tags): a typed call is more reliable
  • low No test case covers injection arriving through data

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 877: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 112 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (9 of 9)
  • +3All 2 scripts are documented

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