Borrowing it
Nothing to install: this file belongs to zernie/vigiles. 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/zernie/vigiles/main/.claude/skills/deep-research/SKILL.mdgit clone --depth 1 https://github.com/zernie/vigilesWrote 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/zernie/vigiles/deep-research)<a href="https://agentmods.dev/skills/zernie/vigiles/deep-research"><img src="https://agentmods.dev/badge/skills/zernie/vigiles/deep-research.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00073 | $0.00754 |
| Opus 5 | $0.00036 | $0.00377 |
| Sonnet 5 | $0.00015 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00075 |
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 8d 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.
<!-- vigiles:ignore-test — project-local workflow skill, not a shipped vigiles surface --> How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep research — gather wide, lose nothing
The failure mode this skill exists to prevent: a big parallel research fan-out gathers 80–100K tokens of detail, each subagent returns a trimmed summary, the orchestrator compresses again into a short brief, and the raw findings live only in ephemeral agent transcripts (scratchpad task outputs) that vanish when the container is reclaimed. Net: expensive research → a thin artifact, detail gone. Don't do that.
The rule: two durable artifacts, never just one
Every nontrivial research effort produces, and SAVES to disk:
- A synthesis — the brief / answer (tables, thesis, recommendation).
- A full appendix — the per-source / per-company / per-angle DETAIL (raw numbers, funding histories, surfaces, dates, verbatim source URLs) the synthesis compressed.
If the saved synthesis is dramatically smaller than what was gathered, the appendix is how you reconcile that — the detail must land somewhere durable, not only in chat or a transcript. "I summarized it in chat" is not saved.
Running a fan-out
- Plan the angles — one subagent per distinct angle (company, source class, sub-question). Tell the user which model each runs on and why (the subagent-model-note rule).
- Mandate write-to-disk in the subagent prompt. Every research subagent must WRITE its full findings to a durable file (e.g. the session scratchpad dir, one file per angle) with sources verbatim, and return only a short pointer + the headline findings. Do NOT rely on the agent's returned summary as the record — it is trimmed by construction and its transcript is ephemeral.
- Capture sources verbatim — every claim carries [number] [source URL] [date] [reported vs estimate]. URLs are the first thing lost in compression; keep them.
- Synthesize from the files, not from memory — read back the written files to build the synthesis, so nothing silently drops.
- Save both artifacts before declaring done — commit/write the synthesis AND the appendix. Then it's saved.
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.
- 8d ago First seen · 58 lines · 73 tokens per session scan C 5acec04cd62c
deep-research is a skill published in the GitHub repository zernie/vigiles (15 stars, last pushed today), licensed MIT. It adds 73 tokens to every session and 754 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
link-check
Verify @file references in AIWG skills and agents against the linking contract — per-file or corpus-wide, with optional auto-fix.
activity-log
Query and manage the unified .aiwg/activity.log chronological record of AIWG-managed workflow operations.
validate-metadata
Validate AIWG extension definitions against the metadata schema and report errors with field names, line numbers, and remediation hints.
eval-agent
Run evaluation tests against an agent to assess quality and archetype resistance.
hook-enable
Enable the AIWG context hook in platform context files without re-deploying.
mention-lint
Lint @-mentions for style consistency and correctness.