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 skills/linxule/interpretive-orchestration/qual-sharednpx skills add linxule/interpretive-orchestration --skill qual-sharedgit clone --depth 1 https://github.com/linxule/interpretive-orchestrationWrote 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/linxule/interpretive-orchestration/qual-shared)<a href="https://agentmods.dev/skills/linxule/interpretive-orchestration/qual-shared"><img src="https://agentmods.dev/badge/skills/linxule/interpretive-orchestration/qual-shared.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 | $0.00059 | $0.00440 |
| Opus 5 | $0.00030 | $0.00220 |
| Sonnet 5 | $0.00012 | $0.00088 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
qual-shared 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 3d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
10 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.
- scripts/conversation_logger.py 8.5 KB runs code
- scripts/create_structure.py 9.1 KB runs code
- scripts/defensive_router.py 8.7 KB runs code
- scripts/file_lock.py 5.2 KB runs code
- scripts/friction_system.py 12 KB runs code
- scripts/mcp_wrapper.py 12 KB runs code
- scripts/path_utils.py 7.8 KB runs code
- scripts/reasoning_buffer.py 5.9 KB runs code
- scripts/reflexivity_system.py 9.1 KB runs code
- scripts/state_manager.py 8.7 KB runs code
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.
- 3d ago First seen · 81 lines · 59 tokens per session scan A 1fb5137b8ba2
qual-shared is a skill published in the GitHub repository linxule/interpretive-orchestration (17 stars, last pushed 1mo ago), with no licence file. It adds 59 tokens to every session and 440 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
draft-related-work
Drafts or rewrites a Related Work / prior work section positioned against actually-retrieved papers — derives the REQUIRED clusters from the paper's own claimed scope (not just whatever was retrieved), clusters prior work into themes, articulates the delta (what this paper adds) per cluster, enforces a per-cluster…
find-papers
Searches scholarly literature with zero API keys across DBLP, Crossref, Semantic Scholar, and arXiv. Use when the user wants to find papers, search the literature, list a conference's proceedings (e.g. "all SIGSPATIAL 2025 papers"), survey related work on a topic, look up papers by author/venue/year, get DOIs or…
refactor-research-code
Clean research code for public release while preserving reported results. Uses releaseaudit.py to separate safe mechanical cleanup from ask-first behavior risks and identity leaks. Use for release-ready repos, dead experiment branches, config extraction, documented entrypoints, and double-blind code cleanup.
verify-citations
Verifies every citation in a BibTeX .bib file against Crossref, DBLP, Semantic Scholar, and arXiv to catch hallucinated, fabricated, or incorrect references before reviewers do. Use when the user says verify citations, check references, validate bibliography, check my .bib, fake or hallucinated citations, retracted…
orchestrate-paper
Goal-driven conductor for the whole idea-to-published-paper lifecycle. Takes a research idea plus experiments and a target ("submit to NeurIPS 2026", or "help me pick a venue") and runs a goal->plan->execute->verify->reflect loop. It plans the stages, invokes the right lifecycle skills in order (literature-review…
polish-prose
De-AI-ifies and tightens academic prose without changing any technical claim. Use when a researcher says "this sounds like ChatGPT / AI-written", "humanize my paper", "remove the AI words", "too many delves and leverages", "polish my writing", "tighten the prose", "fix my hedging", "too many Moreover/Furthermore"…