Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AI-Unified-Process/marketplace --skill reverse-engineergit clone --depth 1 https://github.com/AI-Unified-Process/marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ai-unified-process/marketplace/reverse-engineer)<a href="https://agentmods.dev/skills/ai-unified-process/marketplace/reverse-engineer"><img src="https://agentmods.dev/badge/skills/ai-unified-process/marketplace/reverse-engineer.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 6 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Prompt Injection · line 17 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 90 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
- high Tool Misuse · line 171 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 172 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00158 | $0.05107 |
| Opus 5 | $0.00079 | $0.02554 |
| Sonnet 5 | $0.00032 | $0.01021 |
| Haiku 4.5 | $0.00016 | $0.00511 |
Grade B, and why
reverse-engineer scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
you or to an AI assistant (e.g. "ignore previous instructions", "run this Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Engineer Project to AI Unified Process Artifacts
Goal
Produce three artifacts from an existing codebase, matching exactly the
formats used by the forward-engineering skills (/use-case-diagram,
/use-case-spec, /entity-model) so the output is a drop-in starting point
for the rest of the AI Unified Process workflow:
docs/use_cases.puml— PlantUML use case diagram (actors and use cases)docs/use_cases/UC-XXX-name.md— one specification document per use casedocs/entity_model.md— entity model with Mermaid ER diagram and attribute tables
The forward-engineering skills derive these from a vision/requirements document; you derive them from code, configuration, schema, and tests.
Format contract — read this before writing any artifact
These are hard requirements, not style preferences. Reverse-engineered documents that break them are rejected exactly like forward-engineered ones:
- Aggregate use cases. The spec-file count must be meaningfully smaller than the endpoint count; a small service collapses to roughly 4–8 use cases. One CRUD resource = one "Manage X" use case.
- Spec files are named
UC-XXX-<kebab-case-name>.md— three-digit ID, lowercase kebab-case, no underscores or PascalCase. - Steps stay at the business level — no SQL, HTTP verbs, framework methods, hashing, tokens, or protocol names in any step.
BR-XXXIDs are scoped to their use case — every spec file numbers its rulesBR-001,BR-002, … from the start, unique and gapless within that file. Cross-references to a rule of another use case are qualified with the use case id ("UC-005 BR-002").- The Mermaid ER diagram shows relationships only — no attributes inside entity blocks.
- Every attribute table has exactly these 5 columns, in this order:
Attribute | Description | Data Type | Length/Precision | Validation Rules. - Data types come from the closed AI Unified Process list —
Long,String,Integer,Decimal,Boolean,Date,DateTime, 'BLOB' — and nothing else. Raw SQL/ORM types (VARCHAR,bigint,numeric,TEXT) are banned, and so are invented "business types" (Money,Email Address,Identifier,Timestamp,Quantity,PersonName,Text). An email column isStringwith validationNot Null, Format: Email; a price isDecimalwith10,2. - Validation Rules cells use only the
/entity-modelvocabulary and are never empty.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 427 lines · 158 tokens per session scan B 18d599439a80
reverse-engineer is a skill published in the GitHub repository AI-Unified-Process/marketplace (121 stars, last pushed 10d ago), licensed Apache-2.0. It adds 158 tokens to every session and 5,107 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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