reverse-engineer-rpi▌
boshu2/agentops · updated Apr 8, 2026
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Reverse-engineer a product into a mechanically verifiable feature inventory + registry + spec set, with optional security-audit artifacts and validation gates.
/reverse-engineer-rpi
Reverse-engineer a product into a mechanically verifiable feature inventory + registry + spec set, with optional security-audit artifacts and validation gates.
Hard Guardrails (MANDATORY)
- Only operate on code/binaries you own or have explicit written authorization to analyze.
- Do not provide steps to bypass protections/ToS or to extract proprietary source code/system prompts from third-party products.
- Do not output reconstructed proprietary source or embedded prompts from binaries (index only; redact in reports).
- Redact secrets/tokens/keys if encountered; run the secret-scan gate over outputs.
- Always separate: docs say vs code proves vs hosted/control-plane.
One-Command Example
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py ao \
--authorized \
--mode=binary \
--binary-path="$(command -v ao)" \
--output-dir=".agents/research/ao/"
If you do not have explicit written authorization to analyze that binary, do not run the above. Use the included demo fixture instead (see Self-Test below).
Repo-only example (no binary required):
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py cc-sdd \
--mode=repo \
--upstream-repo="https://github.com/gotalab/cc-sdd.git" \
--output-dir=".agents/research/cc-sdd/"
Pinned clone (reproducible):
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py cc-sdd \
--mode=repo \
--upstream-repo="https://github.com/gotalab/cc-sdd.git" \
--upstream-ref=v1.0.0 \
--output-dir=".agents/research/cc-sdd/"
Invocation Contract
Required:
product_name
Optional:
--docs-sitemap-url(recommended when available; supportshttps://...andfile:///...)--docs-features-prefix(default:auto; detects best local docs prefix, falls back todocs/features/)--upstream-repo(optional)--upstream-ref(pin clone to a specific commit, tag, or branch; records resolved SHA inclone-metadata.json)--local-clone-dir(default:.tmp/<product_name>)--output-dir(default:.agents/research/<product_name>/)--mode(default:repo; allowed:repo|binary|both)--binary-path(required if--modeincludesbinary)--no-materialize-archives(authorized-only; binary mode extracts embedded ZIPs by default; this disables extraction and keeps index-only)
Security audit flags (optional):
--security-audit(enables security artifacts + gates)--sbom(generate SBOM + dependency risk report where possible; may no-op with a note)--fuzz(only if a safe harness exists; timeboxed)
Mandatory guardrail flag:
--authorized(required for binary mode; refuses to run binary analysis without it)
Upstream Ref Pinning (--upstream-ref)
Use --upstream-ref to pin a repo-mode clone to a specific commit, tag, or branch. This makes analysis reproducible and allows golden fixtures to be diffed against a known baseline.
# Pin to a tag (reproducible)
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py cc-sdd \
--mode=repo \
--upstream-repo="https://github.com/gotalab/cc-sdd.git" \
--upstream-ref=v1.0.0 \
--output-dir=".agents/research/cc-sdd/"
# Pin to a specific commit SHA
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py cc-sdd \
--mode=repo \
--upstream-repo="https://github.com/gotalab/cc-sdd.git" \
--upstream-ref=abc1234 \
--output-dir=".agents/research/cc-sdd/"
When --upstream-ref is provided:
- The clone is fetched with
git fetch --depth=1 origin <ref>and checked out toFETCH_HEAD. - The resolved commit SHA is recorded in
output_dir/clone-metadata.jsonfor traceability. - Without
--upstream-ref, a--depth=1shallow clone of the default branch HEAD is used instead.
clone-metadata.json schema:
{
"upstream_repo": "https://github.com/gotalab/cc-sdd.git",
"upstream_ref": "v1.0.0",
"resolved_commit": "<full SHA>",
"clone_date": "YYYY-MM-DD"
}
Contract Outputs (output_dir/)
Repo-mode analysis writes machine-checkable contract files under output_dir/. These files use only relative paths, sorted lists, and stable keys — no absolute paths, no run-specific timestamps — so they can be committed as golden fixtures and diffed across runs.
Primary contract files:
| File | Description |
|---|---|
feature-registry.yaml |
Structured feature inventory with mechanically-extracted CLI, config/env, and artifact surface |
cli-surface-contracts.txt |
CLI surface: commands, flags, help text, framework, language |
docs-features.txt |
Features extracted from documentation (docs say vs code proves) |
clone-metadata.json |
Upstream repo URL, pinned ref, resolved commit SHA, clone date |
Example feature-registry.yaml structure:
schema_version: 1
product_name: cc-sdd
upstream_commit: "abc1234..."
features:
- name: cli-entry
cli:
language: node
bin:
cc-sdd: dist/cli.js
help_text: "Usage: cc-sdd [options] ..."
- name: config-surface
config_env:
config_file: ".cc-sdd/config.json"
env_vars:
- name: CC_SDD_TOKEN
evidence: ["src/config.ts"]
Note: Contract outputs are written by
--mode=repo(or--mode=both). Binary-mode outputs (binary-analysis.md,binary-symbols.txt, etc.) remain directly underoutput_dir/.
Fixture Test Workflow
Golden fixtures allow regression detection: commit a known-good fixture snapshot (contract files alongside the pinned clone-metadata.json), then diff future runs against it.
Running Fixture Tests
bash skills/reverse-engineer-rpi/scripts/repo_fixture_test.sh
This script (implemented in ag-w77.3):
- Reads
skills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/clone-metadata.jsonto determine the pinned upstream ref. - Runs
reverse_engineer_rpi.pyin repo mode with that ref into a temp output dir. - Diffs the generated outputs against the committed golden fixtures (
feature-registry.yaml,cli-surface-contracts.txt,docs-features.txt). - Exits 0 if they match; exits non-zero with a unified diff if they drift.
The test requires network access to clone the upstream repo.
Updating Fixtures
When contracts legitimately change (new flags, new env vars, schema bumps), update the golden fixtures:
# 1. Re-run with the pinned ref to generate fresh contracts
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py cc-sdd \
--mode=repo \
--upstream-repo="https://github.com/gotalab/cc-sdd.git" \
--upstream-ref=<new-tag-or-sha> \
--output-dir=".tmp/cc-sdd-refresh/"
# 2. Copy contracts into the fixture directory
cp .tmp/cc-sdd-refresh/feature-registry.yaml \
skills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/feature-registry.yaml
# 3. Update the pinned clone metadata
cp .tmp/cc-sdd-refresh/clone-metadata.json \
skills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/clone-metadata.json
# 4. Commit the updated fixtures
git add skills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/
git commit -m "fix(reverse-engineer-rpi): update cc-sdd golden fixtures to <new-tag-or-sha>"
Fixture files that must be committed for the test to pass:
skills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/clone-metadata.jsonskills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/feature-registry.yamlskills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/cli-surface-contracts.txtskills/reverse-engineer-rpi/fixtures/cc-sdd-v2.1.0/docs-features.txt
Script-Driven Workflow
Run:
python3 skills/reverse-engineer-rpi/scripts/reverse_engineer_rpi.py <product_name> --authorized [flags...]
This generates the required outputs under output_dir/ and (when applicable) .agents/council/ and .agents/learnings/.
Outputs (MUST be generated)
Core outputs under output_dir/:
feature-inventory.mdfeature-registry.yamlvalidate-feature-registry.pyfeature-catalog.mdspec-architecture.mdspec-code-map.mdspec-cli-surface.md(Node, Python, or Go CLI detected; otherwise a note is written tospec-code-map.md)spec-clone-vs-use.mdspec-clone-mvp.md(original MVP spec; do not copy from target)clone-metadata.json(when--upstream-repois used; records resolved commit SHA)
Binary-mode extras:
binary-analysis.md(best-effort summary)binary-embedded-archives.md(index only; no dumps)
Repo-mode extras:
spec-artifact-surface.md(best-effort; template/manifest driven install surface)artifact-registry.json(best-effort; hashed template inventory when manifests/templates exist)
If --security-audit, also create output_dir/security/:
threat-model.mdattack-surface.mddataflow.mdcrypto-review.mdauthn-authz.mdfindings.mdreproducibility.mdvalidate-security-audit.sh
Self-Test (Acceptance Criteria)
End-to-end fixture (safe, owned demo binary with embedded ZIP):
bash skills/reverse-engineer-rpi/scripts/self_test.sh
This must show:
- feature inventory generated
- registry generated
- registry validator exits 0
- in security mode:
validate-security-audit.shexits 0 and secret scan passes
Examples
Scenario: Reverse-Engineer an Open-Source CLI in Repo Mode
User says: /reverse-engineer-rpi cc-sdd --mode=repo --upstream-repo="https://github.com/gotalab/cc-sdd.git" --upstream-ref=v1.0.0
What happens:
- The script shallow-clones the upstream repo at the pinned tag
v1.0.0and records the resolved SHA inclone-metadata.json. - It scans the repo for CLI entry points, config/env surface, schema files, and artifact manifests, then writes
feature-inventory.md,feature-registry.yaml, contract JSON, and all spec files under the output directory.
Result: A complete feature catalog and machine-checkable feature-registry.yaml are generated under .agents/research/cc-sdd/, ready for golden-fixture diffing.
Scenario: Binary Analysis With Security Audit
User says: /reverse-engineer-rpi ao --authorized --mode=binary --binary-path="$(command -v ao)" --security-audit
What happens:
- The script runs static analysis on the
aobinary (file metadata, linked libraries, embedded archive signatures) and writesbinary-analysis.mdandbinary-embedded-archives.md. - It generates the full security audit suite (
threat-model.md,attack-surface.md,findings.md, etc.) underoutput_dir/security/and runs the secret-scan gate over all outputs.
Result: Binary analysis artifacts plus a validated security audit are produced; validate-security-audit.sh exits 0 confirming all security deliverables are present and secrets-clean.
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| Script refuses to run binary analysis | Missing --authorized flag |
Add --authorized to confirm you have explicit written authorization to analyze the binary. |
clone-metadata.json not generated |
--upstream-repo was not provided |
Pass --upstream-repo (and optionally --upstream-ref) to enable clone metadata tracking. |
| Fixture test diff fails unexpectedly | Upstream repo changed or golden fixtures are stale | Re-run with the pinned ref, copy fresh contracts into fixtures/, and commit the updated golden files (see Updating Fixtures). |
spec-cli-surface.md not generated |
No recognized CLI framework (Node/Python/Go) detected in the repo | Check that the target repo has a discoverable CLI entry point; otherwise the CLI surface is documented in spec-code-map.md instead. |
| Network error during repo clone | Firewall, VPN, or GitHub rate limit blocking the shallow clone | Verify network connectivity, authenticate with gh auth login if the repo is private, or use --local-clone-dir to point at a pre-cloned directory. |
How to use reverse-engineer-rpi on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add reverse-engineer-rpi
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches reverse-engineer-rpi from GitHub repository boshu2/agentops and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate reverse-engineer-rpi. Access the skill through slash commands (e.g., /reverse-engineer-rpi) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★44 reviews- ★★★★★Neel Li· Dec 24, 2024
Registry listing for reverse-engineer-rpi matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Shikha Mishra· Dec 20, 2024
Useful defaults in reverse-engineer-rpi — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Henry Menon· Dec 16, 2024
reverse-engineer-rpi has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Yash Thakker· Nov 11, 2024
reverse-engineer-rpi has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Fatima Mensah· Nov 7, 2024
Useful defaults in reverse-engineer-rpi — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Fatima Kim· Oct 26, 2024
I recommend reverse-engineer-rpi for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Dhruvi Jain· Oct 2, 2024
Solid pick for teams standardizing on skills: reverse-engineer-rpi is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Sep 21, 2024
We added reverse-engineer-rpi from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★James Anderson· Sep 17, 2024
Keeps context tight: reverse-engineer-rpi is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chinedu Patel· Sep 9, 2024
reverse-engineer-rpi fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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