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 yogsoth-ai/de-anthropocentric-research-engine --skill canonicalize-entitygit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/canonicalize-entity)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/canonicalize-entity"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/canonicalize-entity/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/yogsoth-ai/de-anthropocentric-research-engine/canonicalize-entity"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/canonicalize-entity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00027 | $0.00693 |
| Opus 5.5 | $0.00011 | $0.00277 |
| Sonnet 5.5 | $0.00005 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
canonicalize-entity 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 1mo 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
canonicalize-entity
Purpose
Resolve aliases and near-duplicates into canonical entities while preserving provenance and rejecting false merges.
Input contract
required: [raw_entities, entity_schema, alias_evidence]
optional: [synonym_dictionary, organization_relationships, graph_edges, canonicalization_policy]
constraints: [the entity schema is explicit; merge requires semantic identity evidence; preserve provenance and redirect relationships]
Procedure
- Normalize names, identifiers, and aliases under the caller-supplied entity schema.
- Search for near-duplicates and compare definitions, identifiers, and provenance.
- Merge only semantically identical entities into a canonical record and redirect dependent edges.
- Preserve rejected-merge pairs and rationale, then return the canonicalized register.
If canonical entities are stable enough to compare against the target domain, consider map-coverage-space as the next tactic.
Output contract
produces: [canonical_entities, alias_map, merge_decisions, redirected_edges, rejected_merges]
delta_fields: [findings, evidence_updates, decisions, uncertainties]
Quality gates
- Preserve the source merge-candidates gate: scan at least 10 concepts, or all concepts if fewer than 10 exist; a candidate score >7.0 is a merge flag, not proof.
- Before merging aliases, verify pages refer to the same concept rather than merely related concepts; retain inline links and edge redirects where applicable.
- Assignee normalization preserves parent, subsidiary, and acquired-entity relationships rather than collapsing organizations into one name.
Parameterization
The caller must provide the entity type and schema, canonical naming policy, alias/synonym evidence, merge confidence rule, graph-edge policy, and whether the task concerns concepts, assignees, or another entity class.
Failure and counterexamples
Reject merges without semantic identity evidence, threshold-only matches, or records that lose provenance or dependent edges.
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.
- 1mo ago First seen · 71 lines · 27 tokens per session scan A 13abca053fa8
canonicalize-entity is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 2d ago), licensed Apache-2.0. It adds 27 tokens to every session and 693 once invoked, about $0.0001 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 2026-09-24.
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