agentleFS
z91772524-aiz91772524-ai/pojia-next-mac/skills/recover-license-native-e2e/SKILL.md

recover-license-native-e2e

Skill39 starsChanged 34 days agoMITView on GitHub

Recover software serial, machine-code, card-key, activation, offline-license, and server-issued entitlement flows. Use for keygen, registration, license-file, subscription, or activation tasks where Codex must preserve the original executable and DLL feature path and prove native acceptance, clean-restart persistence, hardware prerequisites, and server-dependent behavior end to end.

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read references/workflow.md before substantial analysis.

    line 27
  2. Distinctive:

    Prefer high-level boundary hooks over early instruction-level decompilation.

    line 53
  3. Distinctive:

    Prefer compatible license generation, token/backend emulation, public-key substitution, or a narrow verifier change.

    line 68
  4. Distinctive:

    Never describe an activation as feature-complete solely because a window opened or a permission label changed.

    line 110

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Native License Recovery

Recover the complete decision path from license input to original feature execution. Keep the application's feature implementation in its original EXE, DLL, managed assembly, script bundle, or service client. Treat a changed label, enabled button, or patched return value as an intermediate observation rather than completion.

Start With Evidence

  1. Hash the installer, main executable, relevant DLLs, configuration, and accepted/rejected fixtures.
  2. Preserve originals and write derived artifacts to a separate workspace.
  3. Identify the active runtime before selecting tools: native, .NET, JVM, Python/Nuitka, Electron, packed/virtualized, or mixed.
  4. Prove one narrow path from machine identity or token input to the final internal entitlement.
  5. Initialize the evidence ledger:
python scripts/license_evidence.py init WORKDIR --target TARGET
python scripts/license_evidence.py classify WORKDIR --runtime nuitka --authority offline-client --server-role none

Read references/workflow.md before substantial analysis.

Select the Authorization Profile

  • Offline client: The client creates or verifies a local serial/license. Recover exact bytes, formatting, binding, time policy, cryptography, and permission mapping.
  • Server issued: The server signs or returns an entitlement while original features remain local. Recover the protocol, response parser, refresh and persistence behavior.
  • Hybrid: Local verification and server state both affect the decision. Trace request order and both state machines.
  • Server executed: The server returns actual feature results or private data. A local backend must reproduce that operation; an entitlement-only response does not establish feature parity.

For any networked path, read references/server-dependency.md before implementing a backend.

Recover the Decision Chain

Trace these boundaries in order and record exact values:

identity/input -> normalization -> serialization -> checksum/signature/KDF
-> encrypted or signed artifact -> parser -> native verifier
-> internal entitlement -> original feature dispatch

Prefer high-level boundary hooks over early instruction-level decompilation. Capture encoding, case, whitespace, delimiters, field order, numeric formatting, salts, nonces, tags, signature coverage, clock source, expiry arithmetic, and machine-component selection. Mutate one field at a time and retain at least one accepted and one rejected vector.

For Python/Nuitka applications, read references/nuitka-runtime.md and use the bundled runtime executor when the embedded interpreter remains loaded:

python scripts/nuitka_pyexec.py --pid PID --code-file PROBE.py --report TRACE.json

Preserve Original Feature Execution

Prefer compatible license generation, token/backend emulation, public-key substitution, or a narrow verifier change. Do not replace feature modules with a mock implementation when the original client or DLL already contains the operation.

Prove all of the following:

  1. The original verifier returns the intended internal permission or entitlement.
  2. A clean process start reloads the artifact or token and reaches the same state.
  3. At least one representative original feature calls its original module, assembly, or DLL.
  4. Original artifacts retain their recorded hashes; keep any patched derivative separate.
  5. Hardware, driver, configuration, content, and server requirements are listed independently of authorization.
  6. For a local backend, every required route and server-owned result has parity evidence.

Enforce the Gates

Record evidence as work progresses:

python scripts/license_evidence.py add-fixture WORKDIR --request REQUEST.bin --response LICENSE.bin --expected accepted --label known-good
python scripts/license_evidence.py gate WORKDIR --name native-acceptance --status pass --evidence native-check.json --note "Original verifier returned Full"
python scripts/license_evidence.py verify-integrity WORKDIR
python scripts/license_evidence.py check WORKDIR --profile offline

Completion requires the profile-specific gates in references/validation-contract.md. Keep a gate pending or failed when only UI state is proven, when a clean restart was skipped, when original feature dispatch was not observed, or when a server still owns unimplemented computation/data.

Report

Lead with the outcome, then include:

  • exact target and derived-artifact hashes;
  • recovered transform chain and known-answer vectors;
  • native verifier result and clean-restart result;
  • representative original feature call evidence;
  • server dependency matrix and local-backend coverage;
  • remaining hardware, driver, configuration, content, or service prerequisites;
  • replay commands and evidence paths.

Never describe an activation as feature-complete solely because a window opened or a permission label changed.

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