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 agentmods add commands/uditgoenka/autoresearch/predictgit clone --depth 1 https://github.com/uditgoenka/autoresearchWhat 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 | $0.00013 | $0.00933 |
| Opus 5 | $0.00006 | $0.00466 |
| Sonnet 5 | $0.00003 | $0.00187 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
autoresearch:predict scanned grade A with 0 findings 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 yesterday.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- autoresearch_predict — 98% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE IMMEDIATELY.
Parse Arguments
Extract from $ARGUMENTS:
Scope:or--scope— file globs to analyzeGoal:or--goal— focus area for analysisDepth:or--depth— shallow (3 personas, 1 round), standard (5, 2), deep (8, 3)--personas N— override persona count (3-8)--rounds N— override debate rounds (1-3)--adversarial— use hostile reviewer personas instead of default--budget N— max findings across all personas (default 40)--fail-on <severity>— CI gate: exit non-zero if findings at/above threshold--incremental— reuse existing knowledge files, update only changed files--chain,--<subcommand>
Remaining text not matching flags = goal description.
Setup (if Scope or Goal missing)
AskUserQuestion (single batch): Q1 (Scope): "Which files to analyze?" — suggested globs + entire codebase Q2 (Goal): "What should personas focus on?" — code quality, security, performance, architecture, all Q3 (Depth): "How deep?" — shallow (3 personas, 1 round), standard (5, 2 — recommended), deep (8, 3) Q4 (Chain): "After analysis, chain to?" — debug, security, fix, ship, scenario, no chain If all provided → skip.
Phase 1: Reconnaissance
Scan all in-scope files. Build structured knowledge:
- File inventory with purpose annotations
- Dependency graph (imports/exports)
- API surface (routes, handlers, types)
- Data flow (inputs → processing → outputs → storage)
- Existing test coverage map
Phase 2: Persona Generation
Load references/predict-personas.md for persona definitions.
Default set (5): Architect, Security Analyst, Performance Engineer, Reliability Engineer, Devil's Advocate. Adversarial set (--adversarial): Breaker, Cheater, Scaler, Newbie, Malicious Insider.
Each persona receives: task description + codebase knowledge + their specific evaluation criteria. Personas are isolated — no shared context between them.
Phase 3: Independent Analysis
Each persona analyzes the codebase independently:
- Read relevant code through their lens
- Produce findings with: title, severity, confidence (0-100%), file:line, recommendation
- Max findings per persona: budget / persona_count
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.
- yesterday First seen · 95 lines · 13 tokens per session scan A 25142c252d54
autoresearch:predict is a command published in the GitHub repository uditgoenka/autoresearch (5,966 stars, last pushed 19d ago), licensed MIT. It adds 13 tokens to every session and 933 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Read the specified design doc section.
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thoth:doctor
Alias for status --doctor; strictly audit project health without writing authority.
feature
End-to-end feature/bug-sweep workflow for aitm — understand, reproduce against a real run, explore and build with a hive of parallel agents in this one checkout (never worktrees), path-disjoint slices, verify under Bun AND Node, PR, merge, and (only when asked) release to npm. Tracks in GitHub issues. Reads intent…
merge-pr
Drive an open PR to merge, then advance to the next PR group.