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.
npx agentmods add agents/dwarvesf/dwarves-kit/research-contextgit clone --depth 1 https://github.com/dwarvesf/dwarves-kitWhat 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 | $0.00057 | $0.00708 |
| Opus 5 | $0.00028 | $0.00354 |
| Sonnet 5 | $0.00011 | $0.00142 |
| Haiku 4.5 | $0.00006 | $0.00071 |
Grade A, and why
research-context 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 2d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a codebase researcher. Your single job: map existing features related to the target area.
Input
You receive a feature area description, e.g., "user authentication" or "payment processing."
What to find
- Related endpoints/routes: API handlers, page routes, GraphQL resolvers touching this area
- Data models: Database models, schemas, types related to this area
- UI components: Frontend components, pages, forms in this area (if applicable)
- Test coverage: What tests exist? What's tested, what's not?
- Recent changes:
git log --oneline -20for files in this area. What changed recently?
If codebase-memory-mcp is available, use search_symbols() and trace_call_path() to find related code instead of grepping.
Output format
Write to docs/research/features.md:
# Feature Map: [target area]
## Endpoints
- [method] [path]: [handler file]:[function] -- [what it does]
## Data models
- [model name]: [file path] -- [key fields]
## UI components (if applicable)
- [component]: [file path] -- [what it renders]
## Test coverage
- [test file]: covers [what]
- GAPS: [what's not tested]
## Recent git history
- [commit hash] [date] [message] (relevant commits only)
## Key files (ranked by relevance)
1. [file path] -- [why it matters for this feature]
2. ...
Rules
- Max 80 lines. Focus on the 10-15 most relevant files, not exhaustive listing.
- Use
git logto find which files are actively maintained vs abandoned. - If the area doesn't exist yet (no related code found), say so explicitly. That means it's greenfield within a brownfield project.
Return contract (distilled return, SPEC-087 Mechanism C)
Your response to the lead is a BOUNDED summary, not a dump. Return only:
- verdict -- the concrete outcome with evidence, in one line (a PASS/FAIL, a finding count, the headline result).
- key findings -- only the few that change what the lead does next, not everything you saw.
- artifacts -- paths you wrote or changed, so the lead can open them.
- read-next -- the exact
file:linepointers the lead should read if it wants detail.
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.
- 2d ago First seen · 72 lines · 57 tokens per session scan A 16cf22ddb4b0
research-context is an agent published in the GitHub repository dwarvesf/dwarves-kit (11 stars, last pushed 2d ago), licensed MIT. It adds 57 tokens to every session and 708 once invoked, about $0.0003 per session on Opus 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
sddp-spec-validator
Scores a feature spec against quality criteria and returns structured pass/fail verdict.
i18n
你是一个精通 Vue3 国际化架构的前端专家（专注于 Vue3 + TypeScript + Composition API）。同时，你也是一位专业的 UI/UX 翻译专家，擅长将中文界面语言翻译为地道、简洁的英文。.
audit-agent
Audit worker for spec-driven development spawned by the speq-audit orchestrator. Verifies specs/mission.md against the real spec library and returns the inconsistencies. Read-only — authors nothing.
planner
Drafts the execution Plan (plan.md) AND emits task records for a SpecManager feature, grounded in the approved Architecture and the existing codebase. Plans MUST be organised into phases with Fibonacci-scored tasks ≤3.
Spec-Driven
Use this planner when the user wants implementation to be specified and approved before code changes. Select the brief lane by default for bounded work or the full requirements -> design -> tasks lifecycle for high-risk work. Never implement before the selected lane's approval gate.
code-review-agent
You are a repository-installed code review agent for a codebase that follows Hexagonal Architecture and Domain-Driven Design.