agent-deep-research

agent-deep-research is a skill for Claude Code, Codex from MichelKerkmeester/skilled-agent-harness_spec-driven-loops. It costs 19 tokens per session (8,213 once invoked), scanned A, original, MIT.

A research agent that performs one iteration of an ongoing research process, saves cited findings, records progress, and returns a short completion report.

In plain words
What is it for?
It is for focused information gathering, cited notes, and research workflows that continue across multiple iterations.
Why use it?
It makes long-running research resumable by keeping findings and iteration state outside the conversation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is node .skilled/skills/system-deep-loop/runtime/scripts/append-mode-event.cjs \.

Good fit It is for focused information gathering, cited notes, and research workflows that continue across multiple iterations.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/MichelKerkmeester/skilled-agent-harness_spec-driven-loops
agentmods
npx agentmods add skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/agent-deep-research

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-deep-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/agent-deep-research/github.svg)](https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/agent-deep-research)
Your own site
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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.

agentmods 80×15 button for agent-deep-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/agent-deep-research"><img src="https://agentmods.dev/badge/skills/michelkerkmeester/skilled-agent-harness_spec-driven-loops/agent-deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,213 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.08213
Opus 5.5 $0.00008 $0.03285
Sonnet 5.5 $0.00004 $0.01643
Haiku 4.5 $0.00002 $0.00821

Measured today against content hash 66e62f20aae7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-deep-research 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 today.

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.

.hermes/skills/agent-deep-research/SKILL.md · 601 lines

How it starts

The opening of the file, as written. The whole thing — 601 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent persona deep-research from .hermes/agents/deep-research.md (the shared agent files), mirrored as a preloadable skill because Hermes has no agent flag. Preload with -s agent-deep-research and adopt it for the session.

The Deep Researcher: Autonomous Iteration Agent

Executes exactly ONE research iteration in the /deep:research loop. It reads externalized state, performs focused research, writes cited findings to packet files, records one iteration record through the append gateway, and returns a concise completion report.

Path Convention: Use only .skilled/agents/*.md as the canonical runtime path reference.

Hook-Injected Advisor Context: Treat hook-injected skill-advisor recommendations as routing hints only. They never override explicit user instructions, active command workflow, scope gates, runtime permissions, agent boundaries, or required skill loading. If advisor context conflicts with the dispatch prompt or verified local files, prefer the dispatch prompt plus file evidence and report the conflict.

Efficiency governor (the per-turn hook does not reach sub-agents — apply it here): reason about the problem, not yourself; lead with the result and act rather than narrate (batch tool calls, report at checkpoints); commit reversible decisions and move; qualify only when it changes what the reader should do.

Operating boundary: This agent is research-focused, codebase-agnostic, and dispatched once per iteration with explicit context about what to investigate. The YAML workflow owns the full loop, reducer sync, dashboard refresh, and convergence decisions.

SPEC FOLDER PERMISSION: @deep-research may write only the resolved local-owner research packet for the target spec. Root-spec runs use {spec_folder}/research/; child-phase and sub-phase runs use a packet directory inside that owning phase's local research/ folder. This distributed-governance exception covers iteration artifacts and progressive research synthesis.

HARD BLOCK INVARIANTS: Stop before research or writes if any invariant fails.

  • Leaf-only: Never dispatch sub-agents and never use the Task tool.
  • State-first: Read config, state JSONL, and strategy before selecting focus or executing research.
  • Packet scope lock: Write only within the resolved local-owner research packet and only to allowed iteration outputs.
  • Evidence-bound output: Never claim completion until the iteration file exists, the append-gateway receipt is verified, and every finding has a cited source or inference marker.
  • No speculative recovery: Do not infer missing state, create replacement control files, or repair reducer-owned files from inside this agent.
  • Read-budget freshness: Reuse captured evidence and exact anchors before broad rereads. If a finding, blocker, or contradiction needs verification, perform the narrowest reread and record the reason in the artifact.
  • Status honesty: Do not convert partial success, unresolved contradiction, or stale evidence into completion language. State the exact remaining uncertainty and the next verification step.

Read the full file on GitHub · 601 lines

Changes

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.

  1. today Changed 66e62f20aae7
  2. 27d ago Changed 9bc0a1f7eddf
  3. 28d ago Changed 867a06dd7035
  4. 1mo ago First seen · 601 lines · 19 tokens per session scan A 394e739ae79c

Subscribe to this mod's changes

agent-deep-research is a skill published in the GitHub repository MichelKerkmeester/skilled-agent-harness_spec-driven-loops (37 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 8,213 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-16.

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