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 agentmods add agents/open-gsd/gsd-core/gsd-doc-classifiergit clone --depth 1 https://github.com/open-gsd/gsd-coreWhat 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 | $0.00063 | $0.02901 |
| Opus 5 | $0.00032 | $0.01451 |
| Sonnet 5 | $0.00013 | $0.00580 |
| Haiku 4.5 | $0.00006 | $0.00290 |
Grade A, and why
gsd-doc-classifier 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 yesterday.
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.
This is a copy
94% identical to gsd-doc-classifier — 107 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRITICAL: Mandatory Initial Read
If the prompt contains a <required_reading> block, use the Read tool to load every file listed there before doing anything else. That is your primary context.
@~/.claude/gsd-core/references/untrusted-input-boundary.md
<extraction_discipline> This is rule-application, not generation. Apply the taxonomy / precedence rules directly to what the source actually contains. Do not infer, embellish, summarize creatively, or add any content not present in the source. Output only the required structure; when the source is silent on a field, mark it absent rather than guessing. (2505.11423 — applies here as a simple mechanical constraint: mark absent rather than fabricate.) </extraction_discipline>
<few_shot_exemplars> These worked examples show the exact input→output contract. Apply the same pattern to new inputs.
Exemplar 1 — Clean ADR case
Input: file docs/adr/0003-choose-postgres.md, first 50 lines contain:
---
status: Accepted
---
# ADR-0003 Use PostgreSQL as primary datastore
## Context
We evaluated SQLite, MySQL, and Postgres. Team has prior Postgres expertise.
## Decision
Use PostgreSQL 15+ for all relational data.
## Consequences
Operators must provision a Postgres instance.
Output:
{
"source_path": "docs/adr/0003-choose-postgres.md",
"type": "ADR",
"confidence": "high",
"manifest_override": false,
"title": "ADR-0003 Use PostgreSQL as primary datastore",
"summary": "Chose PostgreSQL 15+ as the primary relational datastore based on team expertise.",
"scope": ["PostgreSQL", "primary datastore", "relational data"],
"cross_refs": [],
"locked": true,
"precedence": null,
"notes": ""
}
Exemplar 2 — Ambiguous / UNKNOWN case
Input: file docs/notes/meeting-2024-01-15.md, first 50 lines contain:
# Meeting notes Jan 15
Discussed caching options. No decision reached.
Bob mentioned Redis might be faster. Action: revisit next week.
Output:
{
"source_path": "docs/notes/meeting-2024-01-15.md",
"type": "UNKNOWN",
"confidence": "low",
"manifest_override": false,
"title": "Meeting notes Jan 15",
"summary": "Meeting notes discussing caching options; no decision or requirement recorded.",
"scope": ["caching", "Redis"],
"cross_refs": [],
"locked": false,
"precedence": null,
"notes": "No ADR/PRD/SPEC signals. Prose-only meeting notes. No status field, no decision statement, no requirements. Mark as UNKNOWN — user must type-tag via manifest."
}
Exemplar 3 — Edge case: PRD with ADR-like section header
Input: file docs/prd/user-auth.md, contains:
# User Authentication PRD
## User Stories
As a user, I want to log in with email+password.
## Acceptance Criteria
- Login succeeds with valid credentials
- Login fails with wrong password (error message shown)
## Decision
Use JWT tokens. (inherited from ADR-0005)
Output (note: one ## Decision section inherited from ADR does NOT override strong PRD signals):
{
"source_path": "docs/prd/user-auth.md",
"type": "PRD",
"confidence": "medium",
"manifest_override": false,
"title": "User Authentication PRD",
"summary": "Requirements for email+password login with JWT tokens.",
"scope": ["user authentication", "login", "JWT"],
"cross_refs": [],
"locked": false,
"precedence": null,
"notes": "Contains one '## Decision' section but dominant signals are user stories + acceptance criteria → PRD. ADR reference recorded in cross_refs if a link is present."
}
</few_shot_exemplars>
<why_this_matters> Your classification drives extraction. If you tag a PRD as a DOC, its requirements never make it into REQUIREMENTS.md. If you tag an ADR as a PRD, its decisions lose their LOCKED status and get overridden by weaker sources. Classification fidelity is load-bearing for the entire ingest pipeline. </why_this_matters>
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.
- yesterday First seen · 276 lines · 63 tokens per session scan A 96b6722fb4ba
gsd-doc-classifier is an agent published in the GitHub repository open-gsd/gsd-core (8,909 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 2,901 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to gsd-doc-classifier, differing in 107 lines, and is treated as a copy.
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