Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pproenca/dot-skills --skill linguistic-semantic-algorithmsgit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/linguistic-semantic-algorithms)<a href="https://agentmods.dev/skills/pproenca/dot-skills/linguistic-semantic-algorithms"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/linguistic-semantic-algorithms/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/pproenca/dot-skills/linguistic-semantic-algorithms"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/linguistic-semantic-algorithms.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 | $0.00130 | $0.02584 |
| Opus 5 | $0.00065 | $0.01292 |
| Sonnet 5 | $0.00026 | $0.00517 |
| Haiku 4.5 | $0.00013 | $0.00258 |
Grade A, and why
linguistic-semantic-algorithms 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 5d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pproenca Linguistic and Semantic Algorithms Best Practices
Reference of 40 algorithms an agent should reach for when extracting structure, meaning, history, or risk signals from source code and commit data. Categories are ordered by insight-per-effort — how much non-obvious truth the technique exposes relative to how easy it is to apply. The first two categories target the highest-leverage questions: what business entities live in this code? and where else does this concept already exist? — questions that grep and intuition cannot answer.
When to Apply
Reach for these algorithms when:
- Orienting in an unfamiliar codebase: PageRank the import graph to find the core, run LDA over identifier tokens to discover business themes, mine change coupling to surface hidden architectural couplings.
- Hunting a bug from a description: BM25 + history prior + embedding re-rank produces a ranked file shortlist far better than grep.
- Scoping a feature: find prior PRs that did similar work via embedding similarity; map the feature's vocabulary against the codebase's domain via TF-IDF and noun-phrase mining.
- Reviewing a refactor: AST-level GumTree diff reveals semantic impact text diff hides; PDG isomorphism finds the "same logic, different code" twin you should also update.
- Auditing risk: hotspots (churn × complexity), bus factor, defect-magnet density, dead-code candidates — together they direct attention to the parts of the codebase that pay back attention.
- Identifying domain entities and bounded contexts: noun-phrase mining + TF-IDF rare-term extraction + Louvain communities + Jensen-Shannon divergence on per-cluster vocabulary.
Rule Categories by Priority
| Priority | Category | Impact | Prefix | Question answered |
|---|---|---|---|---|
| 1 | Concept & Domain Extraction | CRITICAL | concept- |
What business entities live in this code? |
| 2 | Semantic Similarity & Feature Mapping | CRITICAL | sim- |
Where else does this concept already exist? |
| 3 | Architectural Topology | HIGH | graph- |
What is the shape of this codebase? |
| 4 | Co-Change & Temporal Mining | HIGH | mine- |
What hidden couplings does history reveal? |
| 5 | Clone & Duplication Detection | MEDIUM-HIGH | clone- |
Where are we repeating ourselves? |
| 6 | Bug & Feature Localization | MEDIUM-HIGH | local- |
Given a description, where in code? |
| 7 | Identifier Linguistics | MEDIUM | ling- |
How to prepare tokens so the other algorithms work? |
| 8 | Complexity & Risk Metrics | MEDIUM | risk- |
Where is the danger concentrated? |
What ships with it
44 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 13 KB
- assets/templates/_template.md 2.9 KB
- metadata.json 2.4 KB
- references/_sections.md 3.7 KB
- references/clone-ast-gumtree.md 4.2 KB
- references/clone-minhash-lsh.md 4.3 KB
- references/clone-simhash.md 4.4 KB
- references/clone-suffix-array-cpd.md 5.0 KB
- references/clone-zhang-shasha-ted.md 4.9 KB
- references/concept-bounded-context-detection.md 4.9 KB
- references/concept-entity-name-resolution.md 4.7 KB
- references/concept-identifier-cooccurrence-network.md 4.1 KB
- references/concept-lda-topic-modeling.md 3.4 KB
- references/concept-noun-phrase-mining.md 3.4 KB
- references/concept-tfidf-rare-terms.md 3.6 KB
- references/graph-betweenness-bottlenecks.md 3.8 KB
- references/graph-feedback-arcs.md 4.6 KB
- references/graph-louvain-modules.md 4.1 KB
- references/graph-pagerank-core.md 4.0 KB
- references/graph-scc-cycle-tangles.md 3.9 KB
- references/ling-abbreviation-expansion.md 5.3 KB
- references/ling-camel-snake-split.md 4.1 KB
- references/ling-porter-stemming.md 4.3 KB
- references/ling-pos-tagging-identifiers.md 4.8 KB
- references/local-bm25-saturation.md 4.3 KB
- references/local-embedding-bug-text.md 4.8 KB
- references/local-history-prior-localization.md 4.8 KB
- references/local-tfidf-bug-reports.md 4.1 KB
- references/mine-bug-fix-density.md 4.9 KB
- references/mine-bus-factor.md 4.9 KB
- references/mine-change-coupling.md 5.2 KB
- references/mine-codebase-aging.md 4.7 KB
- references/mine-commit-topic-modeling.md 4.9 KB
- references/mine-hotspots-churn-complexity.md 4.9 KB
- references/risk-cognitive-complexity.md 4.8 KB
- references/risk-cyclomatic-mccabe.md 4.0 KB
- references/risk-halstead-volume.md 4.7 KB
- references/risk-shannon-entropy-naming.md 5.3 KB
- references/sim-call-pattern-similarity.md 4.4 KB
- references/sim-codebert-embeddings.md 4.4 KB
- references/sim-cosine-vsm-files.md 3.8 KB
- references/sim-cross-pr-feature-mapping.md 4.0 KB
- references/sim-doc-code-alignment.md 4.5 KB
- references/sim-pdg-semantic-clones.md 4.2 KB
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.
- 5d ago First seen · 125 lines · 130 tokens per session scan A 2c0344b59652
linguistic-semantic-algorithms is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 130 tokens to every session and 2,584 once invoked, about $0.0006 per session on Opus 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 2026-09-03.
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