CC reverse-engineer-anything
Reverse-engineer binaries, firmware, and web apps with REA AgentEnv / Rekayasa balik biner, firmware, dan web apps dengan REA AgentEnv.
Promises to reverse-engineer compiled code—native, managed, JavaScript, packaged, firmware, browser targets. Uses REA and builds AgentEnv bundles from live instances or captures.
Eight modules, no critical errors, safety-score 100. Quality-score 78 is solid; process-score 60 flags gaps in logic or error handling. No scripts—the skill relies entirely on agent integration. In practice, it works as a reference for decompilation methods, not autonomous execution. You'll need to drive the process yourself.
Install if you need artifact analysis and version comparison. Skip it if you expect automation—this is a manual toolkit.
Reverse-engineer binaries, firmware, and web apps with REA AgentEnv / Rekayasa balik biner, firmware, dan web apps dengan REA AgentEnv.
As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, execution cost
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 10155 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 336, 656): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Execution cost. Instruction body is 10155 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 57 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 135: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 57 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.