Answer a bounded question with current cited evidence. Triggers: "research", "investigate this question", "find evidence". (Investigating a repository routes to codebase-recon.)
npx skills add https://github.com/boshu2/agentops --skill research
Answer one bounded question with current evidence. Research informs a caller;
it does not select work, approve a plan, mutate lifecycle state, or decide what
happens next.
required for a useful answer.
use current primary sources.
Use the current agent inline by default. Parallel readers or alternate runtimes
are optional execution choices only when the caller authorizes them. Prior
research, CASS, MS, codebase recon, and pattern mining are advisory sources,
not required phases.
A claim about what code does cites the commit it was observed at, plus
file:line — code moves, and a citation without a revision decays silently into
a claim about a repository that no longer exists. For the working tree, record
the current HEAD and whether the cited file carries uncommitted changes. The
named failure mode is the floating citation: a path and line that resolved
when written, drifted after a refactor, and now lends false authority to a
stale answer. A reader must be able to run git show <commit>:<path> and see
the cited lines; a code claim that cannot survive that replay is reported as
unverified, not asserted.
Research is done when its capability flags are answerable, not when effort
feels sufficient. At the start, derive from the bounded question a short list
of capability statements — "can name the module that owns X, with citation",
"can state whether Y is reachable from Z, or that this is unknown". The stop
condition: every flag is either satisfied with evidence or explicitly reported
unknown with what was searched. Hours spent and files read are not flags. The
named failure mode is effort-shaped doneness — stopping because the search was
long, and shipping an answer whose load-bearing claim was never actually
established. If a flag stays unsatisfiable inside scope, say so and stop;
widening the question mid-search is a new question, and the caller owns it.
When the caller supplies several reports for one bounded question, synthesize
them as evidence inside this same Research invocation:
supplied identifier, title, author/runtime when known, and revision or date
when supplied. Assign a short local label without replacing that identity.
reference. Normalize wording only for comparison; never merge citations or
make agreement erase provenance.
Agreement means independent reports support the same claim. Contradiction
preserves the conflicting claims and evidence. Unknown means the reports do
not establish the fact or the underlying source was not checked. Reports that
repeat one upstream source are agreement in wording, not independent
corroboration; preserve that shared provenance.
question requires it. A report's conclusion is advisory, not authority.
where they disagree, and what remains unknown. Report checked and unchecked
sources, then stop.
Do not recursively launch another Research pass, invent a synthesis umbrella,
or start a new runtime merely because multiple reports exist. Additional readers
remain caller-authorized execution choices, not part of this procedure.
For a quick question, return the cited answer directly. When the caller asks
for a durable artifact, write one report containing:
For a durable synthesis of multiple reports, also include source_ledger and
comparison (agreements, contradictions, and unknowns) as defined by the
output schema. Single-report outputs may omit those optional fields.
Do not emit approval, confidence gates, retry instructions, owner, next action,
or delivery state.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
A practical, jargon-free guide to fp-ts functional programming - the 80/20 approach that gets results without the academic overhead. Use when writing TypeScript with fp-ts library.
Take boshu2/research from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.