# Prospect > Benchmark your project against reputable peer projects and mine them for concrete improvements: survey your repo, clone references locally, extract enforceable mechanisms, audit your own code through each reference's written doctrine, verify every finding independently, and output a verified improvement batch. Use when the user wants to benchmark against comparable projects, mine external repos for practices, or run an outward-looking quality round. - Skill: `samgalanakis/prospect` (Agent Skill) - Install (CLI): `npx skillmds@latest add samgalanakis/prospect` - Raw SKILL.md: https://api.skillmd.com/api/skills/samgalanakis/prospect/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: samgalanakis (https://skillmd.com/u/samgalanakis) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/samgalanakis/prospect --- # Prospect Grade your project against named, inspectable peer projects instead of abstract best practice. Extract mechanisms, not impressions: the lint table, the CI gate, the schema shape, the written policy. Verify every finding before reporting it. ## Arguments Invoked bare, run the full wide round: survey everything, choose the aspects and references yourself. Any arguments are free-form and may mix, in any order: - **aspects** (e.g. `durability`, `testing`, `release engineering`) — scope the round: the survey, reference picks, lens reviews, and findings focus on those aspects only; - **references** (project names, `owner/repo`, or URLs) — these ARE the reference set, slotted under the aspects they best serve. Add a reference the user did not name only when a scoped aspect would otherwise have no coverage, and state what you added and why so the user can strike it. Interpret ambiguous arguments sensibly (a project name is a reference; a quality dimension is an aspect); ask only if genuinely undecidable. Scoped rounds still use the exclusion map. ## The pipeline ### 1. Survey your repo Frame the repo in one paragraph: what kind of artifact it is, its scale, and the two or three concerns that must never break. These feed the aspect list in the next step. Then build the **exclusion map** — the ground no finding may duplicate: - every open issue/ticket in the tracker (id + title), - every settled ruling and documented non-goal (ADRs, project instructions, past reviews). Write it to a file and inject it into every agent you spawn, with the instruction: skip anything it covers; if a finding extends an open item, name the item and state only the delta. This is what makes repeat runs surface new findings instead of re-finding known ones. ### 2. Decompose into aspects, pick references per aspect List the aspects along which your project can be compared. Typical aspects: - the product category (what it is — e.g. agent SDK, CLI, database client); - the language and toolchain (e.g. large Rust workspace); - each load-bearing concern from the survey (e.g. durability, streaming, concurrency, security); - disciplines you want to level up (e.g. testing, release engineering, observability, docs). For each aspect, pick the most respected project *for that aspect*. A reference earns its place by excellence at one aspect; it does not need to resemble your project otherwise — a database with a famous safety culture is a valid durability reference for a web framework. One reference can serve several aspects, and an aspect can have several references. Ask the user for references they want included; fill the remaining aspects with reputable picks and say which aspect each reference covers. A weak reference is still useful: "their CI never runs the test suite" is a negative benchmark worth reporting. ### 3. Clone references locally — always Work from local clones, never from memory, blog posts, or web summaries: ```bash git clone --depth 1 https://github.com/owner/repo.git /tmp/ref- ``` Reuse `/tmp/ref-` if it already exists. Every claim about a reference must be verifiable in its working tree: quote the actual lints table, count the actual assertions, open the actual CI workflow. If a reference cannot be cloned, drop it. ### 4. Extract mechanisms One reader agent per reference, all launched in parallel in the background. Each returns a practices brief scoped to the aspects that reference was picked for — extract whatever is enforceable and transferable there, not a fixed checklist. Good examples of the kind of thing worth extracting: - how a boundary or invariant is actually enforced (a dependency graph shape, a lint config, a custom check in CI), quoted from the tree; - a testing harness or fixture convention that explains why their suite is trusted; - a written doctrine document (style guide, architecture invariants, release checklist) — summarize its actual rules; - a data-model or storage decision that sidesteps a problem you have; - a release/versioning/deprecation policy with teeth; - weaknesses — what not to copy is also a finding. Let the reference steer the brief: a release-craft reference deserves different questions than a durability reference. Ask each reader to end with a short ranked list of adoptable practices. "They care about quality" is not a finding. "They run cargo-semver-checks as a required PR check, decoupled from release tooling, per .github/workflows/x.yml" is. ### 5. Audit your repo through their lenses Do not only read references for ideas to import. Convert each reference's written doctrine into a review of your own repo: a reviewer agent reads the doctrine document first (style guide, safety manifesto, architecture invariants), then audits your codebase against those rules, with the exclusion map in hand. This consistently finds more than a generic "review this repo" prompt, because the reference has already decided what matters and written falsifiable rules. Run at least one doctrine-lens review plus one review of whatever ground previous rounds under-covered. ### 6. Verify every finding No finding ships unverified. The failure mode is a plausible-looking finding a model asserted without checking; one bad claim discredits the whole batch. - A separate verification fan-out re-derives every count, line number, and characterization from the working tree: "byte-identical" gets diffed, "N sites" gets recounted, "never called" gets grepped. - Subtle correctness findings get verified by the strongest available model, or by hand, reading the actual code paths. - Corrections are applied before anything is reported. Expect verification to change results: some claims strengthen, some die. - Verifiers only verify; finders never verify their own findings. ### 7. Output - **Report**: composite verdict; consensus findings with attribution (independent convergence between reviewers is the confidence signal); reference-derived practices ranked by impact; and a "considered, not adopted" list so the next run knows what was already evaluated. - **Tickets** (when the user wants them): one checkable outcome each, verified figures only, detailed evidence below the fold, explicit delta statements against adjacent open tickets, cross-links applied. - **Decisions**: findings that carry real design choices go to the user with the mechanics explained and a recommendation, before anyone implements against them. ## Subagent use Conduct the prospect with subagents: readers, reviewers, and verifiers are independent work — fan them out in parallel rather than working serially, and give each a self-contained prompt (repo framing, exclusion map, scope, output contract). Model selection and orchestration mechanics follow your environment's own agent guidance. ## Repeat runs Each round: refresh the exclusion map (it grew), rotate in new references and new lenses, aim reviewers at ground the last round under-covered, and carry forward the "considered, not adopted" list. A round that finds nothing new in covered ground and something real in fresh ground is the process working.