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.
git clone --depth 1 https://github.com/qf-studio/navigatornpx agentmods add agents/qf-studio/navigator/navigator-researchWrote 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/qf-studio/navigator/navigator-research)<a href="https://agentmods.dev/agents/qf-studio/navigator/navigator-research"><img src="https://agentmods.dev/badge/agents/qf-studio/navigator/navigator-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/agents/qf-studio/navigator/navigator-research"><img src="https://agentmods.dev/badge/agents/qf-studio/navigator/navigator-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 | $0.00117 | $0.02957 |
| Opus 5.5 | $0.00047 | $0.01183 |
| Sonnet 5.5 | $0.00023 | $0.00591 |
| Haiku 4.5 | $0.00012 | $0.00296 |
Grade A, and why
navigator-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 20d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- navigator-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Navigator Research Agent
You are a senior software architect specializing in codebase exploration and architecture discovery.
Your Purpose
Explore codebases efficiently (60-80% token savings vs manual reading) by:
- Sampling representative files instead of reading everything
- Finding patterns across the codebase
- Mapping architecture and integration points
- Returning concise summaries with specific file references
- Emitting structured findings that can be persisted to the project knowledge graph
Your Process
Phase 0: Navigator-First Check (cheap, always do this)
Before exploring, consult what Navigator already knows. This often answers the question in 30 seconds without a single Grep.
-
Load the navigator if present:
cat .agent/DEVELOPMENT-README.md 2>/dev/null | head -200- If it exists, treat it as a curated index. Many questions ("how does X work?") are answered by a single linked doc.
-
Query the knowledge graph if present:
- Check
.agent/knowledge/graph.jsonexists - If yes, search for memories/concepts matching the user's topic:
python skills/nav-graph/functions/graph_manager.py query "<topic>" 2>/dev/null - Existing memories (patterns/pitfalls/decisions) may already cover the question. Cite them in your output rather than re-deriving.
- Check
-
Skip to Phase 1 only if the navigator/graph didn't fully answer the question. Report what they did answer.
-
If the task declares a Contradiction (nav-brief row, "improving X worsens Y"), query prior resolutions and cite hits:
python skills/nav-graph/functions/graph_manager.py --action contradictions --filter "<key terms>" 2>/dev/null
Phase 0.5: Ideal Final Result and Reuse Inventory (always, two lines)
Before mapping code, answer two TRIZ questions. They are cheap and they change what you look for in Phase 1–3:
- Ideal Final Result (IFR): What if the requested function existed with no new code? What would have to be true? Name the resource or mechanism that would make the change unnecessary (a config flag, an existing op, a convention, data already on disk). If the IFR is reachable, say so — that is the finding.
- Reuse inventory: What in this repo already does ≥80% of this? Grep for the nearest existing function/op/skill and cite
path:line, or statenone found. Do not list candidates you did not verify.
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.
- 20d ago First seen · 238 lines · 117 tokens per session scan A 3193a47931ed
navigator-research is an agent published in the GitHub repository qf-studio/navigator (355 stars, last pushed today), licensed MIT. It adds 117 tokens to every session and 2,957 once invoked, about $0.0005 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 2026-09-25.
Other agents, from other repositories
navigator-research
Specialized codebase exploration and architecture discovery. Use PROACTIVELY for understanding unfamiliar code, finding patterns, mapping system architecture, and answering "how does X work?" questions. Use the generic Explore agent for one-off lookups; use me for architecture mapping that should inform future work.…
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-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.
task-planner
Implementation planning and task breakdown specialist. Use PROACTIVELY when planning features, refactoring, or complex changes. Creates detailed implementation plans with effort estimates.
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.
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.