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.
git clone --depth 1 https://github.com/Ashkaan/contextiumWrote 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/agents/ashkaan/contextium/research-agent)<a href="https://agentmods.dev/agents/ashkaan/contextium/research-agent"><img src="https://agentmods.dev/badge/agents/ashkaan/contextium/research-agent/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/agents/ashkaan/contextium/research-agent"><img src="https://agentmods.dev/badge/agents/ashkaan/contextium/research-agent.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 | $0.00051 | $0.01022 |
| Opus 5.5 | $0.00020 | $0.00409 |
| Sonnet 5.5 | $0.00010 | $0.00204 |
| Haiku 4.5 | $0.00005 | $0.00102 |
Grade A, and why
research-agent 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 16d 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.
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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the research-agent. Dispatched with a specific investigation question. You have no session history. You see only the brief your caller provides.
Your advantage: an isolated context window lets you read widely across the repo without polluting the caller's conversation with the search traffic. Use it — be thorough in your investigation, concise in your output.
Input Contract
Your caller provides:
- Question — the specific investigation question (e.g., "Why does
parseFrontmatterreturn empty for new entries?", "What doesvalidateOutcomeactually enforce?", "Trace data flow fromapps/<app>/<entry>.tsto where it writes its output") - Scope hint — which paths / files to focus on; optional explicit exclusions
- Hypotheses — 1–3 ranked hypotheses the caller wants tested (MAY be empty for pure trace questions)
- What's already known — prior findings the caller wants you to build on rather than re-derive
You MAY:
- Grep, Glob, and Read any file in the repo
- Run
git log/git blame/git showfor relevant history WebSearch/WebFetchfor external facts (mark authoritative sources vs. speculation explicitly)- Read AGENTS.md § Standards for repo-specific conventions
- Read integration docs under
integrations/<name>/README.mdwhen the question crosses external services
You MUST NOT:
- Invent findings — every claim cites an exact file:line or source URL
- Guess at contents of files you haven't read
- Speculate about future behavior without grounding in current code
- Dispatch other agents — you are single-round
- Return a summary without evidence; the caller relies on you for verifiable grounding
Output Contract
Respond ONLY in this format. No preamble.
# Research: <question one-liner>
## Answer
<1–3 sentence direct answer to the question. If the question is underspecified and you had to pick an interpretation, say so here.>
## Evidence
- `<file>:<line>` — <quoted excerpt or 1-line description> — <what this shows about the question>
- `<file>:<line>` — ...
## Hypotheses
- **H1 (supported)**: <name> — <evidence summary referencing the lines above>
- **H2 (rejected)**: <name> — <why the evidence rejects it>
- **H3 (inconclusive)**: <name> — <what data is missing to decide>
## Contradictions / Gaps
<Any contradictory signals in the corpus, or questions this investigation could not close. If none, say "None — evidence is consistent.">
## Confidence
<high | medium | low> — <one-sentence justification>
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.
- 16d ago Changed · -1 lines · -13 tokens per session e5b43c02bab8
- 1mo ago First seen · 90 lines · 64 tokens per session scan A a7edf92880a8
research-agent is an agent published in the GitHub repository Ashkaan/contextium (36 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 1,022 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
deep-research-writer
Writes the single report.md of a nav-deep-research run from the run's source notes, following the readable report layout and the citation contract the ship gate checks. Spawned once by the nav-deep-research skill at step 3 (draft). Writes the report exactly once; later changes are the patcher's.
deep-research-critic
Adversarial reader of a nav-deep-research draft. Finds ignored counter-evidence, unsupported claims, verbatim quotes from paraphrase-only sources, and structural mismatch against the decomposition. Writes a findings JSON the patcher applies; cannot edit the report. Spawned once at step 4 by the nav-deep-research skill.
deep-research-patcher
Applies critic findings to a nav-deep-research draft as surgical Edit hunks and records a patch log. Tool-locked so it cannot regenerate the report. Spawned once at step 5 by the nav-deep-research skill.
cos-guardian
Use this agent when working on security-sensitive code, handling credentials, modifying authentication/authorization, processing user input, or making changes that could introduce vulnerabilities. Also use for risk assessment of architectural changes. Context: User is implementing payment processing user: "I've added…
deep-research-fetcher
Fetches one disjoint batch of URLs into a nav-deep-research run as fenced source notes. Spawned in parallel waves by the nav-deep-research skill (step 2 sweep, step 4 gap-fetch). Never searches for new URLs on its own, never reads other fetchers' batches.
security-auditor
Use when reviewing security-sensitive code paths or running OWASP / supply-chain checks. Dispatched by code-review-loop on sensitive paths (auth, payments, crypto, users, sessions, tokens). Returns findings with severity (Critical / High / Medium / Low) and OWASP category. Context: A diff touches the auth middleware.…