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 TimSimpsonJr/magpie --skill entity-extractgit clone --depth 1 https://github.com/TimSimpsonJr/magpieWrote 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/timsimpsonjr/magpie/entity-extract)<a href="https://agentmods.dev/skills/timsimpsonjr/magpie/entity-extract"><img src="https://agentmods.dev/badge/skills/timsimpsonjr/magpie/entity-extract.svg" alt="Measured on agentmods" 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 | $0.00119 | $0.02110 |
| Opus 5 | $0.00060 | $0.01055 |
| Sonnet 5 | $0.00024 | $0.00422 |
| Haiku 4.5 | $0.00012 | $0.00211 |
Grade A, and why
entity-extract 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 8d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
entity-extract
entity-extract is the first Track-B skill. It turns ONE trustworthy ingested document into a REVIEWED, FtM-shaped intermediate -- but only after a mandatory human gate clears every claim. It runs GLiNER entity NER + GLiREL relation extraction, maps the result deterministically to an FtM-shaped graph with per-statement provenance, and emits a followthemoney-free reviewed intermediate.
Engines (all pure or lazy -- nothing heavy imports until you call a real model):
scripts/entity_extract.py-- the pure core:docling_to_extraction_input,extract(),build_intermediate(),ReviewQueue,Statement. stdlib only.scripts/entity_taxonomy.py--resolve(), the entity/relation taxonomy (generic default + the surveillance/flock preset).scripts/entity_models.py-- the lazy GLiNER/GLiREL edge (gliner/glirel/spaCy imported only on first predict).
No .mcp.json ships -- this skill drives the scripts directly.
0. Refuse a non-trustworthy document (checked first)
The ONLY upstream input is a Phase-6 ingest DoclingDocument JSON plus its IngestResult. Normalize it first:
from scripts.entity_extract import docling_to_extraction_input
doc = docling_to_extraction_input(
docling_json,
doc_id=source_sha256, # the ingest source hash
trustworthy_for_extraction=ingest_result_bool,
)
extract() then REFUSES (returns refused=True, an empty queue) when
trustworthy_for_extraction is false -- i.e. the ingest decision was review
or PARTIAL_SUCCESS. Key on the BOOLEAN, never the decision string: this is the
SAME Phase-8 trust seam investigate uses. There is NO override in v1 -- a
non-trustworthy document never auto-extracts.
1. Resolve the taxonomy + extract
from scripts.entity_taxonomy import resolve
from scripts.entity_models import GlinerEntityExtractor, GlirelRelationExtractor
taxonomy = resolve("generic") # or resolve("flock")
result = extract(
doc,
taxonomy=taxonomy,
namespace="<run name>",
entity_extractor=GlinerEntityExtractor(),
relation_extractor=GlirelRelationExtractor(),
threshold=0.4,
)
What ships with it
1 file 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.
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.
- 8d ago First seen · 169 lines · 119 tokens per session scan A 4a4ca4378c32
entity-extract is a skill published in the GitHub repository TimSimpsonJr/magpie (2 stars, last pushed 2mo ago), licensed MIT. It adds 119 tokens to every session and 2,110 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 2026-08-31.
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