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 commands/vrennat/developersdevelopers/researchgit clone --depth 1 https://github.com/vrennat/developersDevelopersWhat 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 | $0.00052 | $0.00392 |
| Opus 5 | $0.00026 | $0.00196 |
| Sonnet 5 | $0.00010 | $0.00078 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
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 yesterday.
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.
What it actually says
/research
Autonomous experimentation for tasks with measurable outcomes. Use when you can quantify success: a benchmark, an accuracy score, a latency number, an A/B comparison.
Procedure
- Restate the question and the success metric in one sentence each.
- Create
.lab/<slug>/if it doesn't exist. This is the experiment workspace. - THINK: form a hypothesis. Write it to
.lab/<slug>/hypothesis.mdwith: claim, predicted measurement, smallest experiment that would prove or disprove it. - TEST: run the experiment. Capture inputs, outputs, and the raw measurement to
.lab/<slug>/runs/<timestamp>.md. - REFLECT: compare measurement to prediction. Was the hypothesis confirmed, refuted, or unclear (variance too high)? Write to
.lab/<slug>/reflections.md. - If unclear: run more samples until variance is acceptable. Report sample size and confidence.
- Iterate from THINK with a refined or replacement hypothesis until the question is answered.
- Final report:
.lab/<slug>/conclusion.mdwith the answer, the evidence, and the variance.
Rules
- Never report a benchmark without sample size and variance.
- If the result could plausibly be noise, say so. Don't rerun fishing for a better number.
- Keep raw run data on disk; don't delete it.
When NOT to use
- The answer is already known and just needs implementation: skip to
/impl. - The question is qualitative (taste, style): brainstorming, not research.
- Pure reading research: just read.
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.
- yesterday First seen · 32 lines · 52 tokens per session scan A d64e2d33c849
research is a command published in the GitHub repository vrennat/developersDevelopers (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 392 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.