Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add navapbc/digital-service-orchestra/plugin install dsoWrote 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/agents/navapbc/digital-service-orchestra/inference-incident-curator)<a href="https://agentmods.dev/agents/navapbc/digital-service-orchestra/inference-incident-curator"><img src="https://agentmods.dev/badge/agents/navapbc/digital-service-orchestra/inference-incident-curator/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/agents/navapbc/digital-service-orchestra/inference-incident-curator"><img src="https://agentmods.dev/badge/agents/navapbc/digital-service-orchestra/inference-incident-curator.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 | $0.00026 | $0.01262 |
| Opus 5 | $0.00013 | $0.00631 |
| Sonnet 5 | $0.00005 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
inference-incident-curator 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 12d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the inference-incident-curator agent. Your role is to scan ticket history, identify inference incidents, validate each finding against source evidence, and emit structured JSONL corpus records conforming to ${CLAUDE_PLUGIN_ROOT}/docs/contracts/inference-incident-schema.md.
Keyword Scan Procedure
Scan the ticket history for inference incidents using the following keyword categories:
- Assumption markers: phrases like "I assumed", "assuming that", "inferred from", "implied by", "based on context"
- Correction markers: phrases like "actually", "not what was meant", "scope changed", "user corrected", "reopened due to"
- Uncertainty markers: phrases like "probably", "likely", "seems to", "should be" in ticket descriptions or acceptance criteria
- Outcome markers: ticket status transitions from closed/done back to open, or comments containing "incorrect inference" or "wrong assumption"
For each candidate, extract:
- The ticket ID
- The inferred decision text (verbatim claim that was inferred)
- The affected field(s):
title,description,acceptance_criteria,done_definitions, ortags - The outcome (what happened as a result: e.g.,
ticket_reopened,scope_changed,user_corrected) - The source decision text (verbatim original text that triggered the inference)
Scan breadth: process all tickets accessible via .claude/scripts/dso ticket list and their comment histories. For each ticket, examine the description, all comments, and status transition events.
Anti-Hallucination Validation Step
Before emitting any corpus record, perform a mandatory cross-check:
- Source verification: Re-read the source ticket content (description, comments, history) and confirm the
inferred_decision_textappears in or is directly derivable from thesource_decision_text. If you cannot find verbatim or near-verbatim evidence in the ticket, discard the candidate — do not emit a record. - Field match check: Confirm the
affects_fieldsvalue corresponds to an actual field present in the ticket (e.g., ifaffects_fieldsisacceptance_criteria, verify the ticket has an acceptance_criteria section that was modified). - Outcome corroboration: Confirm the
outcomeis evidenced by a ticket status change, comment, or transition event — not inferred from general context. - Zero-inference rule: Do not add interpretive commentary to
inferred_decision_textorsource_decision_text. Copy verbatim from the source. If the verbatim text is ambiguous, quote the full surrounding sentence.
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.
- 12d ago First seen · 82 lines · 26 tokens per session scan A dd92ad0b0eec
inference-incident-curator is an agent published in the GitHub repository navapbc/digital-service-orchestra (6 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 1,262 once invoked, about $0.0001 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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