Challenging — Adversarial Review

Adversarial review before shipping. Three paths, each with distinct trade-offs:

• Subagent path (Claude Code, primary). Native sub-Claude via the Task tool — zero API keys, fresh context window, same model. Best when available.
• External API path (claude.ai, Codex, headless). Gemini (cross-model + cross-context) or Anthropic API (cross-context). Costs an incremental API call but gives genuine outside perspective.
• Self path (any environment). The caller assistant inhabits the adversary persona in a dedicated response. Zero cost, retains full subject-matter context from the conversation. Weaker at catching same-session confabulations than fresh-context adversaries, stronger at catching local-convention and factual errors the artifact glosses over. Not a strict downgrade — a different failure-mode profile.

The adversary='auto' resolution (default) picks gemini → claude → self based on available credentials. Callers in Claude Code still use prepare() + Task tool explicitly (subagent is strictly better than self in that environment, and auto-detection of Claude Code is brittle).

Inspired by VDD (dollspace.gay) and Grainulation's anti-rationalization patterns. The drill helper adopts the 5 Whys pattern from Tim Kellogg's open-strix writeup. The self-path persona-inhabitation move is kin to generative-thinking's inversion — commit to the mode before evaluating.

Profiles

Pick the profile matching your artifact. Read only the profile you need — each is self-contained with persona, anti-rationalization table, evaluation criteria, and adversary system prompt.

Profile	Use For	Iteration strategy	File
prose	Blog posts, essays, articles — generic prose competence	parallel replay	references/prose.md
prose-register	Prose with a named voice signature — fidelity check	parallel replay	references/prose-register.md
analysis	Research briefs, comparisons, synthesis	parallel replay	references/analysis.md
code	Scripts, implementations, PRs	parallel replay	references/code.md
recommendation	Technical decisions, architecture choices	parallel replay	references/recommendation.md
philosophers	Arguments, position pieces, design rationales — conceptual-layer audit	parallel replay	references/philosophers.md
drill	5 Whys on one finding from a review	sequential deepen	references/drill.md

One engine, one surface, two iteration strategies. Review profiles iterate in parallel replay — each pass independent, novelty tracked for confabulation. Drill iterates in sequential deepen — each pass takes the chain so far and produces one more why-level until bedrock or max depth, followed by a synthesis pass that extracts root causes.

philosophers and analysis are complements on argument-heavy artifacts: analysis audits the evidentiary layer (source independence, cherry-picking, calibration); philosophers audits the conceptual layer via Socratic elenchus and Aristotelian division (definitional stability, inference validity, is/ought slippage, taxonomy-before-verdict). An artifact can pass one and fail the other.

prose and prose-register are siblings, not redundant. prose evaluates generic prose competence (claims, logic, structure, performed insight) and is explicitly told not to comment on style. prose-register evaluates fidelity to a named voice signature passed via voice=... (positive markers + anti-patterns) and ignores generic competence. Run both passes on voiced prose — they catch different failure modes.

Usage — Claude Code (subagent path, primary)

Two-step protocol: a Python helper builds the prompt, you spawn a subagent via the Task tool, then a parser turns its response into structured findings.

import sys
sys.path.insert(0, '/mnt/skills/user/challenging/scripts')
from challenger import prepare, parse_response

job = prepare(
    artifact=open('/home/claude/draft.md').read(),
    profile='prose',
    context='Blog post about RAG scaling laws',
)

Then invoke the Task tool — subagent_type='general-purpose', prompt=job['prompt'], description='Adversarial review (prose)'. The subagent runs in a fresh context, applies the persona, and returns a JSON message. Pass that message text to the parser:

result = parse_response(subagent_text)
print(result['verdict'])    # SHIP | REVISE | RETHINK
print(result['findings'])   # List of specific issues
print(result['strengths'])  # What to preserve

Why subagents (not the API): no key, no network dependency, fresh context, and the same Claude that's reviewing your work is reviewing it again with no prior bias — but in a clean window. For cross-model diversity (genuinely different blind spots), use Gemini below.

Voiced prose — prose-register (subagent path)

For prose written in a named voice, prose will miss register-specific failure modes by design (its persona is instructed not to comment on style). Use the prose-register profile with a voice=... signature instead — or in addition, since the two profiles target orthogonal failure modes.

voice = """
Corvid voice signature.
Positive markers: short sentences when certainty is high, dry observations,
lead with the answer then context, no throat-clearing.
Anti-patterns: heroic-narrator framing, drama-line-breaks ("Then the part that
almost killed it." floating alone), cliche tells ("shot itself in the foot",
"footgun"), time-scale inflation ("a month ago" when it was yesterday),
performed-significance setups ("What's interesting about this is..."),
RTFM-performed-as-revelation, precious sentimentality in closings.
"""

job = prepare(
    artifact=open('/home/claude/draft.md').read(),
    profile='prose-register',
    context='Blog post for muninn.austegard.com',
    voice=voice,
)
# Task tool → parse_response() as usual.

voice is required for prose-register and rejected for every other profile — pass the signature once, fail loud if it leaks to the wrong call. The richer the signature (with both positive markers and explicit anti-patterns), the more precise the review. A thin signature returns verdict: RETHINK with a finding describing what to sharpen rather than a confident review against an ambiguous spec.

Drill — 5 Whys on a systemic finding (subagent path)

Drill uses the same prepare() / Task / parse_response() protocol as review profiles, but you run the loop yourself — each pass takes the chain so far and produces exactly ONE new {why, because}. When the adversary sets bedrock=true (or you hit max depth), run a final synthesis pass.

from challenger import prepare, parse_response

suspect = next(f for f in result['findings'] if f['severity'] in ('high', 'critical'))
artifact = open('/home/claude/draft.md').read()
ctx = 'Blog post about RAG scaling laws'

chain = []
for depth in range(1, 6):
    job = prepare(artifact, 'drill', context=ctx, finding=suspect, chain=chain)
    # Task tool: subagent_type='general-purpose', prompt=job['prompt']
    step = parse_response(subagent_text)   # {why, because, bedrock, reasoning}
    chain.append({'why': step['why'], 'because': step['because']})
    if step.get('bedrock'):
        break

# Final synthesis pass over the completed chain
job = prepare(artifact, 'drill', context=ctx, finding=suspect, chain=chain, synthesize=True)
# Task tool again, then:
diagnosis = parse_response(subagent_text)
print(diagnosis['chain'])        # [{why, because}, ...]
print(diagnosis['root_causes'])  # usually 3-4 distinct systemic issues
print(diagnosis['direction'])    # compass heading for the process fix

finding accepts either a dict from parse_response() or a free-text description. Patches fix the instance; drills fix the class. Why sequential, not parallel? A single-shot drill lets the model shortcut the whole tree and produces renames instead of explanations; one level per pass forces each "because" to earn its depth. See references/drill.md for when to drill and the anti-patterns to reject.

Usage — claude.ai, Codex, headless scripts (API path)

Where subagents aren't available, call an external model directly.

from challenger import challenge

result = challenge(
    artifact=open('draft.md').read(),
    profile='prose',
    context='Blog post about RAG scaling laws',
    adversary='gemini',     # default — cross-model diversity
)

adversary accepts:

• auto (default) — resolves to gemini > claude > self based on available credentials. Logs the choice.
• gemini — Gemini 3.8 Flash at thinking_level='high'. Cross-model + cross-context. Requires Gemini credentials. (Was 3.1 Pro until 2026-09-03; the Pro tier is off routing, its price/quality is dominated by the later Flash models.)
• claude — Anthropic API. Cross-context, same model family. Do not use this in Claude Code — use the subagent path instead.
• self — NOT runnable via challenge() (raises with a pointer to prepare_self()). Self-challenge requires the caller assistant to produce the adversary response, which a synchronous function cannot do.

Drill uses the same challenge() call with profile='drill'. challenge() runs the whole sequential-deepen loop internally and returns the synthesized diagnosis:

diagnosis = challenge(
    artifact,
    profile='drill',
    context=ctx,
    finding=suspect,          # required for drill
    max_iterations=5,         # optional — defaults to 5 for drill, 3 for review
)
print(diagnosis['chain'], diagnosis['root_causes'], diagnosis['direction'])

Blocking mode (API path, review profiles only)

result = challenge(artifact, profile='analysis', mode='blocking', max_iterations=3)

Loops the adversary until: (a) no actionable findings, (b) novelty rate > 75% (adversary inventing problems — artifact is clean), or (c) max iterations. mode is ignored when profile='drill' — drill always iterates until bedrock or max depth. Subagent-path callers can replicate blocking mode by looping prepare() / Task / parse_response() themselves and tracking findings across iterations.

Usage — self path (same-context adversary)

When neither subagents nor external API credentials are available — or when subject-matter context from the current conversation is load-bearing for the review — use prepare_self(). The caller assistant inhabits the adversary persona in a dedicated response.

from challenger import prepare_self, parse_response

job = prepare_self(
    artifact=open('draft.md').read(),
    profile='analysis',
    context='Cross-domain claim that depends on codebase-specific IEEE-754 conventions',
)
# job is {'system': <adversary system prompt>, 'user': <artifact + context>, ...}

The caller then:

1. Reads job['system'] — the prompt opens with SELF-INVOCATION MODE instructing a full persona switch. Commit to it.
2. In a dedicated response, produces JSON matching the schema described in the system prompt.
3. Passes that JSON string to parse_response().

# After generating the adversary JSON in a dedicated response:
result = parse_response(adversary_json_text)
print(result['verdict'], result['findings'])

When self beats external: the artifact depends on conventions visible only from inside the conversation (codebase invariants, prior decisions, domain terminology established earlier). External adversaries with generic priors issue confident-but-wrong findings in these cases; self retains the context.

When external beats self: the artifact contains confabulations or blind spots the caller already committed to. Fresh context catches these; self inherits them.

When possible, run both. They have orthogonal failure modes.

Drill via self path uses the same loop as the subagent drill: iterate prepare_self(profile='drill', finding=..., chain=...) — produce one {why, because} per dedicated response — append to chain — until bedrock or max depth — then a final synthesize pass.

Verdicts

• SHIP: Clean. Deliver.
• REVISE: Real issues, sound core. Fix and deliver.
• RETHINK: Structural problems. Reconsider approach.

Severity Levels

• critical/high/medium/low: Standard severity — actionable findings that block in blocking mode.
• unverifiable: Adversary flagged something it doesn't recognize (API, pattern, model name) but can't confirm is wrong. Surfaced for awareness but does not block SHIP. Use context to ground the adversary on APIs/patterns it may not know.

Credentials (API path only)

The subagent path needs no credentials. The API path loads from environment or project files:

• Gemini via Cloudflare Gateway (preferred): CF_ACCOUNT_ID, CF_GATEWAY_ID, CF_API_TOKEN from env or proxy.env
• Gemini direct: GOOGLE_API_KEY from env
• Claude API (claude.ai fallback only): ANTHROPIC_API_KEY or API_KEY from env or claude.env

No external skill dependencies. requests is loaded lazily — only the API path requires it.