Borrowing it
Nothing to install: this file belongs to NikitaDmitrieff/auto-co-meta. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NikitaDmitrieff/auto-co-meta/main/.claude/skills/deep-research/SKILL.mdgit clone --depth 1 https://github.com/NikitaDmitrieff/auto-co-metaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nikitadmitrieff/auto-co-meta/deep-research)<a href="https://agentmods.dev/skills/nikitadmitrieff/auto-co-meta/deep-research"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/deep-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nikitadmitrieff/auto-co-meta/deep-research"><img src="https://agentmods.dev/badge/skills/nikitadmitrieff/auto-co-meta/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00088 | $0.08103 |
| Opus 5 | $0.00044 | $0.04052 |
| Sonnet 5 | $0.00018 | $0.01621 |
| Haiku 4.5 | $0.00009 | $0.00810 |
Grade C, and why
deep-research scanned grade C with 1 finding 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 9d ago.
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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- All static instructions, methodology, and templates below this line --> Copies of this mod
1 near-identical copy found in the catalogue:
- deep-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 857 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Core System Instructions
Purpose: Deliver citation-backed, verified research reports through 8-phase pipeline (Scope → Plan → Retrieve → Triangulate → Synthesize → Critique → Refine → Package) with source credibility scoring and progressive context management.
Context Strategy: This skill uses 2025 context engineering best practices:
- Static instructions cached (this section)
- Progressive disclosure (load references only when needed)
- Avoid "loss in the middle" (critical info at start/end, not buried)
- Explicit section markers for context navigation
Decision Tree (Execute First)
Request Analysis
├─ Simple lookup? → STOP: Use WebSearch, not this skill
├─ Debugging? → STOP: Use standard tools, not this skill
└─ Complex analysis needed? → CONTINUE
Mode Selection
├─ Initial exploration? → quick (3 phases, 2-5 min)
├─ Standard research? → standard (6 phases, 5-10 min) [DEFAULT]
├─ Critical decision? → deep (8 phases, 10-20 min)
└─ Comprehensive review? → ultradeep (8+ phases, 20-45 min)
Execution Loop (per phase)
├─ Load phase instructions from [methodology](./reference/methodology.md#phase-N)
├─ Execute phase tasks
├─ Spawn parallel agents if applicable
└─ Update progress
Validation Gate
├─ Run `python scripts/validate_report.py --report [path]`
├─ Pass? → Deliver
└─ Fail? → Fix (max 2 attempts) → Still fails? → Escalate
Workflow (Clarify → Plan → Act → Verify → Report)
AUTONOMY PRINCIPLE: This skill operates independently. Infer assumptions from query context. Only stop for critical errors or incomprehensible queries.
1. Clarify (Rarely Needed - Prefer Autonomy)
DEFAULT: Proceed autonomously. Derive assumptions from query signals.
ONLY ask if CRITICALLY ambiguous:
- Query is incomprehensible (e.g., "research the thing")
- Contradictory requirements (e.g., "quick 50-source ultradeep analysis")
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .gitignore 248 B
- ARCHITECTURE_REVIEW.md 15 KB
- AUTONOMY_VERIFICATION.md 12 KB
- COMPETITIVE_ANALYSIS.md 6.0 KB
- CONTEXT_OPTIMIZATION.md 7.4 KB
- QUICK_START.md 4.9 KB
- reference/methodology.md 14 KB
- requirements.txt 454 B
- scripts/citation_manager.py 5.9 KB runs code
- scripts/md_to_html.py 9.7 KB runs code
- scripts/research_engine.py 16 KB runs code
- scripts/source_evaluator.py 9.3 KB runs code
- scripts/validate_report.py 12 KB runs code
- scripts/verify_citations.py 15 KB runs code
- scripts/verify_html.py 7.6 KB runs code
- templates/mckinsey_report_template.html 10 KB
- templates/report_template.md 13 KB
- tests/fixtures/invalid_report.md 288 B
- tests/fixtures/valid_report.md 3.3 KB
- WORD_PRECISION_AUDIT.md 13 KB
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.
- 9d ago First seen · 857 lines · 88 tokens per session scan C af533b36012a
deep-research is a skill published in the GitHub repository NikitaDmitrieff/auto-co-meta (43 stars, last pushed 2mo ago), licensed MIT. It adds 88 tokens to every session and 8,103 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
browser-automation
Playwright-based browser automation patterns for autonomous web interaction.
a2ui-renderer
Render A2UI (Agent-to-UI declarative surfaces) in CopilotKit v2. Enable the runtime via CopilotRuntime({ a2ui: {...} }), then enable the provider via . Auto-activates via /info — do NOT manually pass renderActivityMessages. createA2UIMessageRenderer ships from @copilotkit/react-core/v2; low-level primitives…
copilotkit-develop
Use when building AI-powered features with CopilotKit v2 -- adding chat interfaces, registering frontend tools, sharing application context with agents, handling agent interrupts, and working with the CopilotKit runtime.
copilotkit-upgrade
Use when migrating a CopilotKit v1 application to v2 -- updating package imports, replacing deprecated hooks and components, switching from GraphQL runtime to AG-UI protocol runtime, and resolving breaking API changes.
dws
A command-line guide for DingTalk, a workplace platform with chat, documents, calendars, approvals, tasks, and other business tools.
clip-hand-skill
Expert knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing.