Agent Skills

research

researchboshu21.5K installs

Trace code or test a recurring pattern to answer one cited question. Use when: uncertainty needs evidence. Not for external feature teardowns; use reverse-engineer.

Install

npx skills add https://github.com/boshu2/agentops --skill research
SKILL.md

Research

Answer the caller's bounded question with cited evidence. Choose ordinary investigation, repository tracing or pattern evidence according to the question; these are optional modes, not a sequence. A quick answer needs no report file. Plan owns unified discovery and resumption when the question is part of shaping a change; return this cited answer to that existing intent without restarting its interview or taking over caller choices.

Investigation

  1. State the question and the decision it informs. Reuse the accepted scope and identify what evidence would answer it; do not expand the objective mid-search.
  2. Inspect the smallest relevant sources. For changing external facts, use current primary sources. Verify search hits against the actual source.
  3. Distinguish observation, inference, contradiction and unknown. Every material claim cites evidence; source agreement does not erase shared provenance.
  4. Lead with the answer, then show evidence and remaining gaps. Each part of the question is answered or explicitly unknown with the searched scope disclosed.

Code claims cite the observed commit plus file:line. For uncommitted content, state HEAD and the changed-file status; do not claim the working bytes can be replayed from HEAD. Keep source identity and freshness visible. Search output, CASS, MS and prior reports are leads, not authority or required phases. Use the current agent by default; additional readers and runtimes require caller selection or existing authorization.

For several supplied reports, retain each source's identifier, author/runtime when known and revision/date. Compare claims as agreement, contradiction or unknown while preserving their original evidence. Repeated quotations of one upstream source are not independent corroboration. Verify decisive claims at their source and return one synthesis; do not launch recursive synthesis passes.

Repository tracing

For a repository model or audit, start from its declared entry points in docs, build manifests or command help and verify them against executable paths. Follow a relevant flow through entry, domain logic, integration and tests; prefer a completed trace to a shallow directory inventory. Report an interrupted trace at its exact file/line and explain what is missing. Choose a useful lens such as persistence, authorization, CLI, build or test without requiring a sweep of every lens.

An inline investigation may use dirty working-tree evidence with explicit limits. When a durable codebase-recon.v1 pack is selected, its stricter contract applies:

  • Write codebase-recon.json and a cited codebase-recon.md companion at the caller's chosen location, default .agents/scratch/codebase-recon/<run-id>/. Keep mental model, bounded audit, pattern evidence and synthesis distinct.
  • Bind the exact current full commit OID, at least one complete baseline flow, claims with kind, confidence and evidence, and inspected/uninspected scope. Fact and inference citations resolve to repository-relative regular files at that commit; the companion report includes line references. Unknowns remain explicit.
  • The manifest report names the companion and its lowercase SHA-256. The companion has one <!-- codebase-recon-report.v1 --> marker and manifest_commit, manifest_mode, flows_sha256, claims_sha256, and coverage_sha256 markers; section digests hash the jq -cS output for each section, including its trailing newline.
  • Discover validated priors with skills/research/scripts/codebase-recon/validate-output.sh --repo-root <target> --discover-priors. Prefer a verified delta when it answers the request. Delta evidence needs a valid ancestor chain, baseline_verified: true and the exact changed paths between the prior and current commits; do not relabel a directory scan as delta.
  • Run skills/research/scripts/codebase-recon/validate-output.sh --repo-root <target> <recon.json> before handoff. It checks both artifacts and rechecks their identities, HEAD and source state; dirty source outside .agents/ cannot satisfy this commit-bound pack. Return a validation failure without disguising it as a completed recon pack.

Preserve earlier .agents/recon/<run-id>/ packs and their exact cited identities. Prior discovery checks both legacy and current roots; never move or delete old proof to match a new layout. See the recon scenarios.

Pattern evidence

For a recurring implementation shape, test whether the similarity represents a reusable rule. Record replayable searches, examined hits and exclusions. Align independent implementations by their role in the behavior, then separate required invariants, legitimate variation and incidental syntax. Copies of one lineage do not count as independent evidence.

A pattern-mining.v1 promotion needs at least three distinct anchored exemplars, a candidate formed before inspecting a separate holdout, a passing holdout and successful back-application of every refinement to the original exemplars. Every invariant needs supporting alignment. Otherwise preserve the result as outcome: hypothesis with route: no-action; do not package weak evidence as a rule.

For this selected durable mode, write pattern-mining.json to .agents/scratch/pattern-mining/<run-id>/ or an authorized caller location and run skills/research/scripts/pattern-mining/validate-output.sh <pattern.json>. Preserve the schema's outcome, exemplars, invariants, variations, incidental, holdout, back_application and route fields. The compatibility route value operationalize on a valid promotion refers to Skill Builder's distillation mode; it is not a retired skill invocation or automatic dispatch.

Recommend the least committed useful shape: no action, a reference/checklist line, a template, helper or gate. A gate needs demonstrated cost of violation, not merely recurrence. Research returns evidence; adoption remains an explicit caller decision. See pattern scenarios.

Output and boundaries

Return a cited answer directly unless a durable output was requested or the selected evidence contract requires one. Ordinary durable reports follow findings.json: question, scope, answer, evidence, contradictions, unknowns, checked and unchecked areas. Multi-report synthesis also retains source_ledger and comparison. Selected recon and pattern modes retain their own validated formats instead of forcing them into this schema.

Use only authorized sources and destinations. For restricted or mined episode material, follow Memory's access and storage boundary. Research selects no work, owns no merged context store, mutates no lifecycle state and issues no semantic verdict. The native caller owns implementation, judgment and completion of the authorized outcome.

Related skills

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers