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name: recon-workbench description: "Analyze and report authorized evidence using Recon Workbench (rwb) workflows. Use when you need authorize/plan/run/summarize flows and evidence-backed reporting for web apps or OSS repos." metadata: source_repo: https://github.com/jscraik/Agent-Skills source_rev: 7e31061c353c94746910d239ae122900cc5324fb-dirty source_dirty: "true" source_dirty_paths: utilities/recon-workbench/references/evals.yaml, utilities/skill-creator/scripts/run_skill_evals.py, design/better-icons/

Recon Workbench (rwb)

Recon Workbench is a production-grade forensic evidence collection platform with policy-driven authorization, comprehensive validation, and supply chain integrity.

Answer with sections titled exactly: Outputs and Procedure (include authorization notes).

When to use

• Running Recon Workbench CLI flows (uv run python -m rwb, or legacy ./recon wrapper)
• Creating authorization artifacts and probe plans for authorized targets
• Summarizing evidence-backed findings and reports with artifact citations

Compliance

• Follow docs/agents/scope-safety.md, docs/agents/cli.md, docs/agents/dev-workflow.md, and docs/agents/ai-governance.md
• Follow AGENTS.md and linked agent docs under docs/agents/
• Use docs/reference/GOLD_STANDARD.md for compliance gates
• Apply docs/reference/AUTHORIZATION_CHECKLIST.md before any run
• Apply docs/reference/DATA_HANDLING.md for redaction/retention
• Evidence-only claims: every finding must cite an artifact path under data/runs/...

Purpose

Create or evolve a unified, controlled interrogation workflow that: (1) authorizes targets, (2) plans probes, (3) collects deterministic artifacts, (4) generates evidence-backed findings, and (5) validates integrity with SHA256 manifests.

Philosophy

• Evidence over inference: If you cannot cite an artifact, mark it as a hypothesis
• Least privilege: Start with static inventory, escalate only when justified
• Safety first: No circumvention, no DRM bypass, no private user data access
• Decision-ready outputs: Minimize noise, maximize traceability
• Explain escalations: Document the why behind each escalation step

Pre-action Questions

Before building or inspecting, clarify:

1. Authorization: Do we have explicit permission to analyze the target?
2. Target type: web app or OSS repo?
3. Goal: Inventory, behavior map, API surface, regression diff, or patch plan?
4. Evidence outputs: What artifacts are required (JSON schemas, reports, traces)?
5. Escalation limit: Maximum allowed level (read_only < instrumentation < escalation)?

Core Constraints (Non-Negotiable)

• No circumvention: No DRM bypass, no cracking, no private data access
• Evidence-only: Every finding must cite an artifact path under data/runs/...
• Observation-first: Prefer static analysis; use dynamic only when authorized
• Authorized tools only: For deeper visibility, request debug builds and use LLDB/Instruments/logs
• Web/React caution: Component inspection only on authorized apps with documented permission
• Redact by default: Scrub secrets from logs, snapshots, and reports

CLI Commands (rwb)

Primary entrypoint from repo root: uv run python -m rwb <command>. Secondary wrapper: ./recon <command> (legacy CLI; prefer rwb workflows).

Core commands (see docs/agents/cli.md and docs/reference/CLI_REFERENCE.md for flags):

Target Kinds

Probe Sets

Predefined probe sets live in probes/catalog.json. Common sets (not exhaustive):

• macos-baseline, ios-baseline
• web-baseline, web-stimulus
• oss-baseline, oss-full

Escalation Levels

• read_only: Static analysis, no code execution
• instrumentation: Log capture, tracing, non-invasive monitoring
• escalation: Debug builds, LLDB, dynamic analysis (requires explicit authorization)

Scope Configuration

Create scope.yaml to set organizational defaults:

Copy# Disallow dangerous probes
disallowed_probes:
  - "debug.lldb_backtrace"

# Limit escalation level
max_escalation_level: "instrumentation"  # read_only < instrumentation < escalation

# Require authorization
require_authorization: true

Inputs

• target_id: Unique identifier for the target
• target_kind: One of macos-app, ios-sim, ios-device, web-app, oss-repo
• target_locator: Path, URL, bundle ID, or repo identifier
• probe_set or probes: Predefined probe set or custom probe list
• authorization: Authorization artifact (required when scope enforces authorization)
• run_dir: Output directory for artifacts (prefer under data/runs/)

Outputs

Structure: data/runs/<target>/<session>/<run>/ (or an explicit --run-dir under data/runs/)

• raw/ - Probe artifacts (logs, dumps, traces, HARs)
• manifest.json - SHA256 hashes for integrity verification
• derived/findings.json - Schema-valid findings with evidence citations; include schema_version
• derived/report.md - Human-readable summary with artifact paths
• derived/report.json - Machine-readable report (when generated); include schema_version when schema-bound

Procedure

1) Check Toolchain

Copyuv run python -m rwb doctor --json

2) Create Authorization (required by scope)

Copyuv run python -m rwb authorize \
  --target-id myapp \
  --target-kind macos-app \
  --target-locator "/Applications/MyApp.app" \
  --output authorization.json

3) Generate Probe Plan

Copyuv run python -m rwb plan \
  --target-id myapp \
  --target-kind macos-app \
  --target-locator "/Applications/MyApp.app" \
  --probe-set macos-baseline \
  --authorization authorization.json

4) Execute Probes

Copyuv run python -m rwb run \
  --plan-file probe-plan.json \
  --run-dir data/runs/myapp/

5) Generate Findings + Report

Copyuv run python -m rwb summarize \
  --run-dir data/runs/myapp/

Validation

Fail fast: stop at the first failed validation gate, fix the issue, and re-run the failed check before proceeding.

Copy# Validate probe catalog
python scripts/validate_catalog.py --catalog probes/catalog.json

# Validate a manifest
python scripts/validate_manifest.py data/runs/myapp/manifest.json

# Validate evidence paths in findings
python scripts/validate_evidence.py data/runs/myapp/derived/findings.json data/runs/myapp/

Escalation Ladder (Worst-Case Path)

1. Static inventory (safe, read-only)
2. Baseline run (minimal interaction)
3. Stimulus run (targeted action)
4. Diff (baseline vs stimulus)
5. Advanced observation (approved tools only; stop if protections block)

Stop conditions:

• Goals are met with evidence
• Further steps require circumvention or exceed authorization
• Signals flatten (no new findings across two successive probes)

Evidence Discipline

• Every finding must cite one or more evidence paths
• Summaries must list commands used + artifact locations
• If evidence is insufficient, request additional probes rather than speculating
• Use manifest.json to verify artifact integrity in data/runs/...

Build Mode (Tooling Design)

When creating or evolving the workbench:

• Design schemas in schemas/ with JSON Schema validation
• Add probes to probes/catalog.json with target kinds and timeouts
• Implement probe scripts in scripts/probes/
• Define probe sets for common workflows
• Update AGENTS.md with agent instructions
• Add validation scripts to scripts/validate_*.py

Inspect Mode (Evidence Collection)

When analyzing a target:

1. Confirm authorization and target type
2. Select appropriate probe set
3. Execute probes and collect artifacts
4. Generate findings with evidence citations
5. Validate all artifacts and evidence paths
6. Produce report with artifact links

Variation Rules

• Vary probe depth by authorization level and target risk profile
• Vary artifact collection based on target type (Apple, web, OSS) and goal
• Avoid repeating the same probe sequence across unrelated targets
• Prefer different variations when signals flatten

Empowerment Principles

• Operators: Explicit stop conditions and safe rollback options
• Teams: Multiple probe paths when trade-offs exist
• Stakeholders: Clear evidence links and decision-ready summaries
• Reviewers: Direct artifact pointers for verification

Anti-Patterns to Avoid

• Acting without explicit authorization or documented scope
• Skipping evidence capture while reporting conclusions
• Using intrusive probes when static inventory suffices
• Escalating by default instead of justifying each step
• Treating unknowns as confirmed facts
• Relying on inferred behavior without artifacts

Example Prompts

• "Design a probe set for React web app component inspection"
• "Validate the evidence paths in this findings.json"
• "Generate a SHA256 manifest for this run directory"

Remember

The agent is capable of extraordinary work in this domain. These guidelines unlock that potential—they don't constrain it. Use judgment, adapt to context, and push boundaries when appropriate.

Resources

Documentation

• README.md - Project overview and quickstart
• docs/reference/GOLD_STANDARD.md - Gold Industry Standard compliance
• AGENTS.md - Agent instructions and workflows
• docs/guides/SECURITY.md - Security policy and vulnerability reporting
• config/scope.example.yaml - Scope configuration template

Schemas

• schemas/authorization.schema.json - Authorization artifact structure
• schemas/probe-plan.v2.schema.json - Probe plan validation
• schemas/findings.v2.schema.json - Findings structure
• schemas/manifest.v2.schema.json - Integrity manifest structure

Probe Catalog

• probes/catalog.json - All available probes and probe sets

Validation Scripts

• scripts/validate_catalog.py - Validate probe catalog
• scripts/validate_manifest.py - Validate integrity manifest
• scripts/validate_evidence.py - Validate evidence paths in findings

MCP Integration

• scripts/mcp_server.py - MCP server for AI agent integration

Constraints

• Redact secrets/PII by default.
• Avoid destructive operations without explicit user direction.

Anti-patterns

• Avoid vague guidance without concrete steps.
• Do not invent results or commands.
• Do not add features outside the agreed scope.