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 poemswe/co-researcher --skill multi-source-investigationgit clone --depth 1 https://github.com/poemswe/co-researcherWrote 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/poemswe/co-researcher/multi-source-investigation)<a href="https://agentmods.dev/skills/poemswe/co-researcher/multi-source-investigation"><img src="https://agentmods.dev/badge/skills/poemswe/co-researcher/multi-source-investigation/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/poemswe/co-researcher/multi-source-investigation"><img src="https://agentmods.dev/badge/skills/poemswe/co-researcher/multi-source-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00834 |
| Opus 5.5 | $0.00009 | $0.00334 |
| Sonnet 5.5 | $0.00005 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
multi-source-investigation 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. Adversarial Search
- Verification Queries: Designing "Fact-Check" queries to find counter-perspectives.
- Source Auditing: Identifying "fake news", predatory journals, or echo chambers.
2. Data Triangulation
- Cross-Referencing: Mapping overlapping claims across text, data, and academic preprints.
- Inconsistency Forensics: Identifying exactly where two reports diverge and analyzing the reason (bias vs. data).
3. Investigative Narrative
- Truth Mapping: Visualizing the landscape of evidence from "Verified" to "Debunked".
- Evidence Weighting: Assessing the "Preponderance of Evidence".
<source_resolution>
When a sub-claim rests on academic work (a study, paper, or preprint), verify it through the database backends owned by the literature-review skill, not through web search alone: uv run <literature-review-dir>/scripts/openalex_cli.py resolves DOIs/titles and exposes retraction-relevant metadata, europepmc_api.py fetches life-science full text and citation graphs, read_paper.py retrieves full text for any DOI/arXiv/PMCID. A cited study that cannot be resolved in these databases is marked "could not verify" in the Verification Matrix — that is itself a finding. Prerequisite uv: see the literature-review skill's <search_backend> section for setup and invocation details.
</source_resolution>
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 · 75 lines · 23 tokens per session scan A c031ba3739ac
multi-source-investigation is a skill published in the GitHub repository poemswe/co-researcher (135 stars, last pushed 3d ago), licensed MIT. It adds 23 tokens to every session and 834 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 2026-08-30.
Other skills, from other repositories
academic-research-suite
ARS-Codex research, writing, manuscript review, study screening, and experiments. Use for deep research, literature or systematic reviews, meta-analysis, research questions, drafts, revisions, roadmaps, abstracts, citations, integrity checks, peer review, research-to-paper. Screen records: sr-screener. Citation…
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…
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…
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…
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.
study-exemplars
Studies exemplar papers from a target venue and produces an original style-and-structure brief — fetches best-paper awardees and top-cited papers on demand (DBLP, Semantic Scholar, Unpaywall, arXiv, open-access ACM DL) and analyzes section architecture, contribution framing, evaluation patterns, and figure/table…