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recon

CI PyPI Python License OpenSSF Scorecard

Point recon at a domain and get its public technology and identity footprint: email security, mail and identity providers, SaaS indicators, and certificate-transparency findings. No credentials, no API keys, no active scanning. Ships as a CLI, versioned JSON, and a local MCP server for agent tools. Lookup time depends on public-provider latency and whether certificate transparency enrichment is enabled.

See the Output

Synthetic recon output in a modern Linux terminal

The panel above is generated, not captured: scripts/generate_terminal_demo.py drives recon's real formatter over a deterministic, no-network fixture for the fictional Example Industries Ltd. It shows the shape of a full-signal result, every row a rich target can fill, and no live lookup of reserved example.com reproduces it. No real organization is depicted. Other project fixtures use IETF reserved .invalid namespaces.

Accessible text transcript
demo@recon:~$ recon example.com    # synthetic fixture, not a captured run
Example Industries Ltd
example.com
──────────────────────────────────────────────────────────────────────────────
  Provider     Microsoft 365 + Proofpoint gateway
  Tenant       a1b2c3d4-e5f6-7890-abcd-ef1234567890 • NA
  Tenant domain example-industries.onmicrosoft.example.com
  Auth         Federated
  Confidence   ●●● High (4 sources)


Services
  Email            Microsoft 365, Proofpoint, DMARC reject, DKIM,
                   SPF strict, MTA-STS enforce
  Identity         Okta
  Cloud            Cloudflare (CDN/edge), AWS Route 53 (DNS)
  Security         Wiz Security
  Data & Analytics Snowflake, Datadog
  Collaboration    Slack, Atlassian (Jira/Confluence), GitHub, Zoom
                   Evidence roles: --explain


High-signal related domains
  login.example.com, support.example.com, status.example.com

Insights
  Federated identity observed; identity-vendor indicators: Okta
  Email security: observed controls: DMARC reject, DKIM, SPF strict, MTA-STS

Defensive use only. Posture review, vendor diligence, architecture review. See docs/legal.md.

Quick Start

macOS / Linux:

curl -fsSL https://raw.githubusercontent.com/blisspixel/recon/main/scripts/install.sh | bash

Windows (PowerShell):

powershell -NoProfile -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/blisspixel/recon/main/scripts/install.ps1 | iex"

The installer sets up recon for your user account, including uv and Python 3.14 when needed. No administrator access or existing Python setup is required for a fresh install. Open a new terminal afterward.

Already have Python 3.11 through 3.14? These work too:

pip install recon-tool
# Or install in an isolated tool environment:
uv tool install recon-tool
pipx install recon-tool

If your system Python is externally managed, use the installer above or a virtual environment.

recon --version      # offline install check
recon doctor         # check installation, latest release, and online connectivity
recon update         # update through the original package manager
recon update --check # check for a newer release without installing

On Windows, recon update starts a background update after the running launcher exits. It prints the path to a local progress and result log. Once complete, run recon --version to confirm.

The scripts install an exact published version, preserve a sole existing uv or pipx owner, and explain how to recover ambiguous or unmanaged installs. For a fixed installer, download a release tag source archive and review scripts/install.sh or scripts/install.ps1 before running it. Artifact verification is covered in the consumer verification recipe.

Before the first lookup, know what leaves your machine. recon makes DNS queries that recursive and authoritative DNS infrastructure may observe. Its only default request to a target-owned endpoint is the standards-defined MTA-STS policy fetch; Google CSE and BIMI certificate probes run only when --direct-probes is explicitly enabled. See ADR-0011.

Run a real review by replacing the placeholder with the domain you want to review:

recon "<domain-you-want-to-review>"

For a real lookup showing the output shape, you can use recon example.com. recon ships no offline demo mode: this is a real lookup command, but an ordinary lookup may reuse a recent result from the default 24-hour cache. Pass --no-cache for fresh collection. Reserved names such as example.com return a panel of stray public residue from unrelated test configurations, including a meaningless display name, at High confidence. It shows the shape of the output, not a result about any organization.

A domain is a query coordinate, not proof of one organization or product: recon reports observations, not verdicts. That is the caution to keep beside every row the panel shows.

Install, update, uninstall, and first-run detail: docs/getting-started.md.

Start with Your Workflow

You are trying to Start here What you get
Review one namespace recon "<domain>" A compact, hedged panel of observed mail, identity, service, and confidence signals
Create a deterministic evidence handoff recon review "<domain>" One fresh, role-neutral NamespaceReviewBundle with collection validity, evidence-linked candidates, and a standing scope statement
Explain or hand off a finding recon "<domain>" --explain Per-source status, lineage-qualified rules, and retained-evidence counts; use --json --explain for the structured evidence graph
Review an operator-supplied set recon batch domains.txt --json --include-ecosystem --summary --summary-schema 2.2 One portfolio evidence bundle with ordered typed results and errors, observable ecosystem overlaps, and an aggregate cohort summary
Work through an agent recon mcp install --client=<name> Local typed tools, resources, and prompts without a hosted recon service

Defenders can follow the evidence-first review workflow. Consultants and analysts should treat every multi-domain set as operator supplied: recon describes observable similarities and differences but does not infer ownership, control, or a corporate relationship.

NamespaceReviewBundle v1 provides a role-neutral handoff for one namespace: one fresh, lookup-result-cache-bypassed baseline, evidence-linked review candidates, explicit bounded collection and failure states, and one deterministic human rendering. Use recon review "<domain>" from the CLI or build_review_bundle(domain) through MCP. Both surfaces return the same v1 artifact; the CLI renders it as Markdown unless JSON is requested.

What recon Is Good For

Need Use recon for Use something else when
Fast external stack context Passive DNS, identity-endpoint, CT, SaaS, and posture indicators You need authenticated tenant inventory or asset-management truth
Defensive review or vendor diligence Hedged observations and evidence traces you can verify You need vulnerability scanning, exploit checks, or host-level facts
Automation-friendly output Stable JSON, batch mode, delta mode, and local MCP tools You need dashboards, scheduling, or report generation built in

recon reports observations, not verdicts. Public channel ceiling: docs/limitations.md. How to report a result without overstating it: docs/reporting-observations.md.

Correlation without overclaiming

recon is designed around a harder question than record collection: when may several incomplete public observations support one claim, and when must the result stay unresolved? The correlation model is the deeper design contract for that question. It separates observed facts, deterministic deductions, and model-relative diagnostics; treats provenance and abstention as first-class; and keeps ownership, causation, complete-inventory, and unvalidated robustness claims outside the result.

The numerical diagnostics have a narrower meaning than their precision may suggest. slug_confidences measures evidence strength; a Bayesian posterior such as 0.9314 is conditional on manually encoded model assumptions, not a validated 93% chance of product use. Its uncertainty band is a display heuristic, not a confidence or credible interval. No current claim family has the independent validation needed for general calibration claims; see statistical assurance. The general robustness machinery in correlation section 5 remains research.

v2.15 and v2.16 closed a five-round presentation-drift class. Independent testers installed the published package, never read the source, and kept finding the same issue: a decision applied to one renderer and not the others. The current engine surface and core contracts are stable. Those findings and their fixes are in CHANGELOG.md and in ADR-0015 through ADR-0017.

The living work is the fingerprint catalog. Vendors add, rename, and retire the public patterns recon detects, so a rule with no re-check is a slow source of false positives and negatives. Useful contributions are scoped catalog changes with a current exact vendor reference (or an independent disclosure-safe basis), a genuine verified review date, synthetic positive/lookalike/sparse fixtures, and provenance tests. A vendor-looking string alone is not sufficient support. CONTRIBUTING.md has the schema, the validation command, and what is deliberately out of scope. The freshness loop and its coverage floor live in docs/catalog-strategy.md. For an agent-assisted maintainer pass, use the corpus planning and catalog-round workflow. It separates offline selection, explicitly bounded collection, candidate refinement, and untouched holdout evaluation. Increased classification coverage does not by itself establish accuracy or justify promotion.

The next improvement loop combines documented catalog coverage with automated checks for duplicate evidence, deceptive lookalikes, failed sources, and output agreement. Local implementation within an authorized task can proceed without human approval steps. Per-slug evidence strength now counts each exact source-type/value pair once, even when multiple rules match it; distinct pairs still use the existing heuristic. The acceptance loop sets the next work and its executable pass conditions.

Common Commands

recon example.com                              # default panel
recon example.com --explain                    # evidence trail
recon example.com --gaps                       # neutral hardening prompts
recon example.com --plain                      # panel as linear text (screen readers, grep)
recon example.com --plain --full               # every field, linear
recon example.com --json                       # structured record
recon review example.com                       # deterministic namespace review
recon review example.com --output review.json  # save the validated v1 artifact
recon example.com --explain-dag --explain-dag-format mermaid   # Bayesian model DAG
recon batch domains.txt --json                 # batch JSON array
recon batch domains.txt --json --include-ecosystem --summary --summary-schema 2.2
                                                # portfolio evidence bundle
recon delta example.com                        # diff vs local cache
recon capsule capture example.com -o run.json  # caller-owned replay artifact
recon mcp install --client=cursor              # wire MCP into a client

More flags: docs/cli-surface.md. JSON contracts: schema · review bundles · stability · operational contract.

Versioned JSON remains recon's structured runtime contract. The Open Knowledge Format v0.2 projection is deferred until a named consumer justifies the mapping. recon does not emit OKF, and any future OKF view would be additive rather than a replacement for JSON. Caller-owned JSON observation capsules are documented in docs/observation-capsules.md, with the decision boundary in ADR-0014.

docs/surface-inventory.json, docs/cli-surface.md, and recon://surface-inventory are generated discovery context and drift guards, not stable runtime API contracts. ADR-0007 records the promotion gate.

Use with an AI agent (plugin / MCP / skill)

Wire recon into Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, Kiro, or any MCP-compatible client:

recon mcp install --client=claude-desktop
# also: claude-code, cursor, vscode, windsurf, kiro
recon mcp doctor

Restart the client after installation, then run recon doctor --mcp, recon mcp doctor, and recon doctor --client=<name> in that order. Start with manual tool approvals. Treat agents as untrusted input. Full setup: docs/mcp.md. Per-client scaffolds and skills: agents/. The PyPI package installs the CLI and MCP runtime. Repository agent scaffolds and plugin files are not included in the wheel or release assets.

The current scaffolds use each client's native configuration. In particular, the source-checkout-only Claude Code plugin uses Claude Code's client-specific layout; it does not claim conformance with the portable Agent Plugins v1.0.0 specification. A complete-surface portable candidate now lives under agents/agent-plugin/ and passes network-free validation against the exact pinned v1.0.0 schemas, whose bytes still match the Published specification. That is not a client compatibility or conformance claim. Representative-client evaluation remains incomplete, so use recon mcp install --client=<name> as the released setup path. The aggregate evaluation status and stop conditions are recorded here.

You say What the agent should do
"Recon example.com" Call lookup_tenant (or recon example.com) and return the panel-style summary
"What does example.com run for email and identity?" Same lookup; lead with MX/IdP/tenant facts and confidence
"Why do you think that?" Re-run with explain / provenance (--explain or explain=true)
"Compare example.com and example.net" compare_postures or two lookups side by side
"Any public hardening gaps?" find_hardening_gaps after a lookup - hedged "Consider" notes only

Example chat. This transcript uses the same synthetic Example Industries fixture as the illustration above, so it shows the shape of a full-signal answer rather than what a live example.com lookup returns:

You: Recon example.com and tell me what you observe.

Agent: Public evidence for example.com (fictional demo):

  • Mail: Microsoft 365 delivery path plus Proofpoint gateway; DMARC p=reject, SPF strict, MTA-STS enforce.
  • Identity: Federated; Okta indicator via CNAME.
  • Tenant: GUID present, region NA; confidence high from four sources.
  • Also seen: Cloudflare and Route 53 infrastructure, Wiz, Snowflake, Datadog, Slack, Atlassian, GitHub, and Zoom public indicators.

These are public-record observations, not a security grade or proof of active product use. Say if you want evidence detail or a gap review.

Guidance: agents/claude-code/skills/recon/, AGENTS.md.

Optional Cloud Access

Local execution is the default, and the project does not operate a hosted service. For teams that want shared remote access, the repository includes a draft authenticated container and Cloud Run Terraform starting point. It is intended to be directionally useful, not a validated production deployment. Operators own deployment, identity, data handling, cost, and operations.

Docs

Topic Link
Glossary of recon's terms docs/glossary.md
Install and first commands docs/getting-started.md
Reporting a result without overstating it docs/reporting-observations.md
How it works docs/how-it-works.md
Known weak areas and conservative wording docs/weak-areas.md
Catalog growth and freshness loop docs/catalog-strategy.md
Observation capsules docs/observation-capsules.md
Correlation model docs/correlation.md
MCP and agents docs/mcp.md, agents/
Full docs index docs/README.md
Roadmap ROADMAP.md · docs/roadmap.md · docs/strategic-gap-audit.md
Changelog CHANGELOG.md
Security SECURITY.md · docs/security.md

Research and publication pointers (maintainer track, not the product core): docs/submission-freeze-checklist.md, validation/2026-06-30-submission-freeze-local-proof.md, docs/public-label-snapshot-decision.md (public lists as robustness checks rather than population rates), and docs/m365-tenancy-decision.md.

Development

Repository and release tasks use uv 0.11.17. The broader >=0.11.8,<0.13 project constraint exists for dependency-update tooling, not as the reproducible maintainer toolchain.

uv self update 0.11.17
uv --version                # must report uv 0.11.17
uv sync
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg   # required; see below
uv run python scripts/release_readiness.py --allow-dirty
uv run python scripts/check.py

The commit-msg hook is a separate installation step and is not optional. Attribution trailers live in commit metadata rather than in a diff, so no file-content hook can see them, and a trailer that reaches a pushed branch cannot be removed afterward: GitHub retains the head commit of every pull request under refs/pull/*/head, where the repository owner cannot delete it. AGENTS.md states the rule.

uv run python scripts/check.py is the canonical local gate: lint, type checks, coverage-gated tests, generated-artifact and catalog checks, text and link hygiene, interface and claim checks, and size and complexity ratchets. It mirrors the blocking core CI gate; hosted scheduled and supply-chain workflows remain separate checks.

Project hygiene: keep examples reserved and synthetic, keep validation artifacts aggregate-only, and avoid dead code or placeholders. See CONTRIBUTING.md.

License

Apache 2.0. Free to use, build on, fork, and share. See LICENSE for the full terms.

About

Map a domain's public technical footprint with provenance, confidence, and explicit uncertainty. Passive by default, machine-readable, and built to report evidence, not guesses.

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