Borrowing it
Nothing to install: this file belongs to Luqueee/kivgraph. 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/Luqueee/kivgraph/main/.claude/skills/adding-a-language/SKILL.mdgit clone --depth 1 https://github.com/Luqueee/kivgraphWrote 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/luqueee/kivgraph/adding-a-language)<a href="https://agentmods.dev/skills/luqueee/kivgraph/adding-a-language"><img src="https://agentmods.dev/badge/skills/luqueee/kivgraph/adding-a-language/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/luqueee/kivgraph/adding-a-language"><img src="https://agentmods.dev/badge/skills/luqueee/kivgraph/adding-a-language.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 420 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00090 | $0.05626 |
| Opus 5.5 | $0.00036 | $0.02250 |
| Sonnet 5.5 | $0.00018 | $0.01125 |
| Haiku 4.5 | $0.00009 | $0.00563 |
Grade A, and why
adding-a-language 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 — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adding a language to Kivgraph
The rule that costs the most to discover
internal/facts/semantic.go:483:
func definitionProvenance(language Language) Provenance {
if language == LanguageDart {
return DartAnalyzerDefinition
}
return PythonIndexerDefinition
}
That was the shape until Java arrived: two functions, both if Dart, else Python. A language added through the payload route and not added here
published every definition as PYTHON_INDEXER_DEF and every use as
PYTHON_INDEXER_USE. Nothing failed. The provenance is legal,
canonicalProvenanceValues accepts it, stage.integrity passes with 0 invariant violations, the golden probes pass, doctor goes green. The graph
is wrong in the one field that says where a fact came from, and nothing but
reading it would have told you.
It is a table with no default now, and NormalizeSemantic refuses a
payload whose language has no provenance. That check is not decoration:
Set.Validate would not have caught it, because it only rejects an empty
provenance under an EXACT confidence and DEFINES is StructuralCertain.
Add your two constants, or your first test fails at the door.
The same shape repeated in the indexer, where the fallthrough was TypeScript, and that one is fixed too -- see The branch that used to decide what you are.
So the work is not writing a loader. The work is finding the branches that already decide what your language is without being asked. Two of them are now failures instead of defaults, which is the only reason this file is shorter than it was. Assume there are more.
Two routes, and the one you want
| Route A: the payload | Route B: your own normalizer | |
|---|---|---|
| The producer emits | facts.SemanticPayload as JSON |
whatever it likes |
| Graph semantics written by | facts.NormalizeSemantic |
you |
| Who took it | Python, Dart, Java, C# | Go, TypeScript, Rust |
| Cost | internal/pythonloader/loader.go is 178 lines, python-worker/index.py is 258 |
internal/facts/golang.go is 747 lines, typescript.go 937, rust.go 534 -- each with its own Input, Report, unit type, cache branch and counters, on top of a loader |
| Contract | docs/protocol/semantic-facts-v1.md |
none; it is code |
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 · 438 lines · 90 tokens per session scan A fda3f3181a22
adding-a-language is a skill published in the GitHub repository Luqueee/kivgraph (23 stars, last pushed yesterday), licensed Apache-2.0. It adds 90 tokens to every session and 5,626 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
codestory-grounding
Use when discovering repository structure, tracing relationships, analyzing change impact, or validating CodeStory retrieval or an installed plugin through the CodeStory plugin MCP. Skip only for bounded inspection or editing of named files that does not require those operations.
omnigraph
Operate OmniGraph graphs and deployments. Use for .pg schemas, .gq queries, OmniGraph CLI commands, file:///s3:///az:// graph URIs, cluster.yaml, operator config, bearer-authenticated servers, graph-backed knowledge or memory, Blob values, embeddings, branches, commits, and change feeds. Apply especially before schema…
roam
Codebase comprehension via roam-code CLI. Use when exploring codebases, planning modifications, debugging failures, assessing PR risk, or checking architecture health. Triggers on: understanding project structure, pre-change safety checks, finding symbols/files, blast radius analysis, affected tests, health scoring…
code-graph
This skill should be used when understanding code structure, finding dependencies between functions/classes, tracing call graphs, or exploring code relationships. Trigger phrases include 'code graph', 'call graph', 'who calls', 'what calls', 'find dependencies', 'code structure', 'inheritance', 'find paths'.
usage
Query LeanKG knowledge graph for code understanding. Use when exploring a codebase, finding functions, tracing dependencies, or calculating impact radius.
code-knowledge-graph
Codebase'i knowledge graph olarak analiz et. Dependency, call graph, hotspot analizi.