AC tinker-backlink-audit
Discover all inbound links (backlinks) to a domain, subdomain, or GitHub repo, then classify each as "ours" (we created/control the source) vs "organic" (someone else). Use when the user asks to find/audit backlinks or inbound links to a site, check who links to a domain or a GitHub repo, separate self-made links from organic ones, or refresh an inbound-links graph. Wraps four sources (GitHub repo referrers, a list of URLs you found, the backlinks.sh Common-Crawl API, and a Google Search Console CSV export) behind one classify-and-report CLI. Two of the four need no account at all; the one optional API key is stored in your OS keychain and cleared by --logout. Built for the TinkerClaw fork — github.com/globalcaos/tinkerclaw. See Permissions, Data Flow & Consent.
Discover all inbound links (backlinks) to a domain, subdomain, or GitHub repo, then classify each as "ours" (we created/control the source) vs "organic"…
As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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
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low Risky intent
intent-offensive-securitySKILL.md:15Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)security: "A read-mostly link classifier. Two of its four sources (urls, gsc-csv) make no network call at all; `github` shells out to the `gh` CLI you already authenticated, and `backlinks` is the onl
detector
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "repository" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 11 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2964 tokens
- 100Progress reporting. Reports progress
- low The response is described with custom markup (6 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 772: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.