Borrowing it
Nothing to install: this file belongs to vlasenkoalexey/tpu_performance_autoresearch_wiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vlasenkoalexey/tpu_performance_autoresearch_wiki/main/.claude/skills/wikify-connect-repo/SKILL.mdgit clone --depth 1 https://github.com/vlasenkoalexey/tpu_performance_autoresearch_wikiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/wikify-connect-repo)<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/wikify-connect-repo"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/wikify-connect-repo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/wikify-connect-repo"><img src="https://agentmods.dev/badge/skills/vlasenkoalexey/tpu_performance_autoresearch_wiki/wikify-connect-repo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.01295 |
| Opus 5.5 | $0.00050 | $0.00518 |
| Sonnet 5.5 | $0.00025 | $0.00259 |
| Haiku 4.5 | $0.00013 | $0.00129 |
Grade A, and why
wikify-connect-repo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wikify-connect-repo
A single ingest produces a silo (wiki/<subdir>/<slug>/ with overview.md + concepts/ +
catalog/). This skill wires silos together on the concept axis, inline, as a normal wiki
— through the concept pages the host already curates (wiki/concepts/*.md). The concept page links
down to each repo's implementation page; each implementation links up to the concept. No
side-table, no new page type, no _connect/ artifact — just links on existing pages.
Selective by design. Connecting every concept to every repo drowns the pages. So the human picks which concepts to connect at this phase — that is the whole point of the interactive step.
The Python/LLM split is hard here too. Candidate proposal + the link insertion are pure Python
(wikify connect). The only judgment is which concepts to connect (and, optionally, pruning a
stray name-match) — done at the ask below.
Preconditions
- The wiki has ≥2 ingested silos (the first repo has nothing to connect to) and a concept
vocabulary directory (
wiki/concepts/*.md). If either is missing, STOP — nothing to do. - Run from the wiki root (where
wiki/andconfig/live). Non-interactive/batch: do only--refresh(regenerate already-connected concepts); never auto-connect new ones.
Procedure
-
Propose (deterministic; no model, writes nothing). Run:
wikify connectIt prints each vocabulary concept that has candidate implementations across the silos (most-implemented first), the repos involved, and which concepts are already connected (
✓). A candidate istag(an explicitconcepts:frontmatter match — authoritative) or a name/token match. Nothing is written yet. -
Ask what to connect (interactive — the selective step). Show the human the proposed concepts and ask which to connect. Recommend the highest-value ones for the wiki's lens (e.g. the Pallas kernels + optimization techniques), not all of them — the goal is a useful spine, not a fully-connected graph. If a chosen concept's candidate list has an obvious name-collision (open the silo page to check when unsure), note the
repo/rel-pathto drop via--exclude.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 89 lines · 126 tokens per session scan A db195b4da127
wikify-connect-repo is a skill published in the GitHub repository vlasenkoalexey/tpu_performance_autoresearch_wiki (58 stars, last pushed 3d ago), licensed MIT. It adds 126 tokens to every session and 1,295 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
wikify-connect-repo compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| wikify-connect-repovlasenkoalexey/tpu_performance_autoresearch_wiki | Skill | 58 | 3d ago | MIT | A | 126 | Claude Code |
| customer_support_agentLeeroo-AI/leeroopedia-mcp | Skill | 14 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| ml_inference_optimizationLeeroo-AI/leeroopedia-mcp | Skill | 14 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| leeroopedia-mcpLeeroo-AI/leeroopedia-mcp | Skill | 14 | 1mo ago | MIT | A | 42 | Claude Code, Codex |
| self_evolve_ragLeeroo-AI/leeroopedia-mcp | Skill | 14 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| llm_post_trainingLeeroo-AI/leeroopedia-mcp | Skill | 14 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| trulens-evaluation-setuptruera/trulens | Skill | 3.6k | yesterday | MIT | A | 17 | Claude Code, Codex |