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geronimo-iiageronimo-iia/agent-skills/skills/oracle/SKILL.md

oracle

Skill2 starsChanged 3 months agoMITView on GitHub

Second-model review pattern: bundle a prompt plus relevant files and send to a separate model for cross-validation, debugging, design checks, or independent analysis. Use when you want an opinion from a different model than the one currently running — debugging hard problems, validating architecture decisions, or getting a fresh read on a refactor.

  • Reads credentials
  • Installs packages

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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 54 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Run code2prompt --help for full flag reference.

    line 59
  2. Distinctive:

    When the problem may need multiple rounds, write a prompt that stands alone:

    line 86
  3. Distinctive:

    Bottom: all context files needed (entrypoints, configs, key modules, docs)

    line 90
  4. Very specific:

    No secrets: never attach .env, key files, auth tokens.

    line 97
  5. Distinctive:

    Advisory only: verify outputs against the actual codebase before acting.

    line 98

“Common” is measured: kept by at least 6 in 10 of the 54 edited versions of this file we could read. “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

Oracle — Second-Model Review

Bundle your prompt + relevant files into one "one-shot" request and send to another model. Treat all outputs as advisory: verify against the codebase and tests before acting.

When to use

  • Stuck on a hard bug and want a second opinion without sharing full context
  • Architecture decision that benefits from a fresh model's read
  • Refactor plan you want cross-validated before executing
  • Any situation where model A is too familiar with the problem to see it clearly

Approaches

Option A: code2prompt (recommended)

Rust CLI — single binary, token counting, git integration, Handlebars templates.

cargo install code2prompt
# Bundle dir → clipboard (default)
code2prompt src/

# Save to file
code2prompt src/ --output-file /tmp/bundle.txt

# Include/exclude patterns (glob)
code2prompt src/ --include "*.rs" --exclude "*.test.rs"

# Show token count before sending
code2prompt src/ --tokens

# Include git diff in bundle
code2prompt . --diff

# Custom Handlebars template
code2prompt src/ --template templates/review.hbs

Project-level config: .c2pconfig at repo root.

Run code2prompt --help for full flag reference. TUI available for interactive selection.

Option B: Agent tool with model override

Within Claude Code, spawn a subagent on a different model tier:

// In a Workflow script
const review = await agent(
  `Review this design: <context>. Question: <question>`,
  { model: 'opus', label: 'second-opinion' }
)

Writing a high-signal prompt

Any second-model review starts with zero project knowledge. Include everything needed to answer cold:

  1. Project briefing — stack, build/test commands, platform constraints
  2. Where things live — key directories, entrypoints, config files
  3. Exact question — what you tried, the error text verbatim
  4. Constraints — "don't change X", "must keep public API", "perf budget"
  5. Desired output — "return patch plan", "list risky assumptions", "give 3 options with tradeoffs"

Exhaustive prompt pattern (long investigations)

When the problem may need multiple rounds, write a prompt that stands alone:

  • Top: 6–30 sentence project briefing + current goal
  • Middle: concrete repro steps + exact errors + what you already tried
  • Bottom: all context files needed (entrypoints, configs, key modules, docs)

Make the prompt re-runnable — the model has no memory of prior exchanges.

Discipline

  • Tight file set: fewest files that contain the truth. Whole-repo dumps add noise, not signal.
  • No secrets: never attach .env, key files, auth tokens. Redact before sending.
  • Advisory only: verify outputs against the actual codebase before acting. The model cannot run your tests.

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