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Get Started Free →Add a new team member. Describe what you need and ATeam infers the config.
.claude/skills/hashgraph-online-add-member/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -37% | 0% |
Add a new team member from a natural language description.
Check for .agenteam/config.yaml, .agenteam.team/config.yaml, or legacy agenteam.yaml. If all are missing:
mkdir -p .agenteamcp <plugin-dir>/templates/agenteam.yaml.template .agenteam/config.yamlpython3 <runtime>/agenteam_rt.py generateExtract role details from the user's natural language request. Examples:
name: performance_engineer, focus: profiling + optimization
name: security_auditor, focus: vulnerabilities + auth
name: docs_writer, focus: documentation, write_scope: docs/**
name: devops_engineer, focus: pipelines + deployment
Infer as much as possible from the description:
| Field | How to infer | |-------|-------------| | name | Snake_case from the role title | | description | From user's description | | responsibilities | 3-5 items inferred from the role's domain | | participates_in | Match to pipeline stages: research, strategy, design, plan, implement, test, review | | can_write | Yes if the role creates/modifies files; no if it only analyzes | | write_scope | Infer from what the role writes (docs, src, tests, configs) | | model | Omit to inherit platform default. If the user requests a pin, inspect codex debug models and choose only from the live catalog. Use evaluation evidence to justify a strong model for demanding analysis or a smaller model for bounded worker tasks. Model is a personal override — share-config strips it. | | reasoning_effort | high for analysis roles, medium for execution/writing roles | | system_instructions | Generate focused instructions from the role's domain |
Before confirming, count the current roles:
bashpython3 <runtime>/agenteam_rt.py roles list
"Your team will have N] roles. Teams above 12 can increase coordination overhead and make role selection harder. Consider extending an existing role's system_instructions instead. Proceed anyway? (yes / cancel)"
Also check: if the team will have more than 6 roles, note: "Codex defaults to 6 concurrent agent threads. To run more agents in parallel, set agents.max_threads in your Codex config.toml."
Present the inferred role as a summary and ask for confirmation:
Here's your new team member:
Name: performance_engineer
Focus: Profiling, bottleneck analysis, optimization
Stages: review, implement
Writes to: src/** (optimization patches)
Model: inherited default (or `gpt-5.4` if you want to pin it)
System instructions:
You are the performance engineer on an AgenTeam. Your primary job is
to identify bottlenecks and optimize critical paths...
Add to team? (yes / adjust)If the user says "adjust" or requests changes, update the fields and re-confirm. Do not ask field-by-field -- keep it conversational.
Read the current .agenteam/config.yaml (or legacy agenteam.yaml) and add the new role under roles:.
Write the full role block including:
descriptionresponsibilitiesparticipates_incan_write and write_scope (if applicable)model and reasoning_effortparallel_safe (true for read-only roles, false for writers unless scoped)system_instructionsIf the role participates in a pipeline stage, also add it to the appropriate pipeline.stages[].roles list.
bashpython3 <runtime>/agenteam_rt.py generate
Show the user:
.codex/agents/<name>.toml@ateam ask <name> to <task>$ateam:assign <name> "<task>".agenteam/config.yaml anytime to adjust this role."| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 19,087 | 15,425 | -19% | 1 | 1 | 0% | 2,342 | 1,399 | -40% | 0 | 0 | — |
case-02 | fail→fail | 14,830 | 17,671 | +19% | 1 | 1 | 0% | 383 | 1,750 | +357% | 0 | 0 | — |
case-03 | fail→fail | 15,612 | 15,019 | -4% | 1 | 1 | 0% | 1,620 | 1,382 | -15% | 0 | 0 | — |
case-04 | fail→fail | 20,026 | 15,382 | -23% | 1 | 1 | 0% | 1,464 | 1,556 | +6% | 0 | 0 | — |
case-05 | fail→pass | 17,678 | 19,003 | +7% | 1 | 1 | 0% | 2,020 | 2,432 | +20% | 0 | 0 | — |
case-06 | fail→fail | 21,269 | 17,168 | -19% | 1 | 1 | 0% | 2,535 | 1,632 | -36% | 0 | 0 | — |
case-07 | fail→fail | 22,270 | 17,428 | -22% | 1 | 1 | 0% | 2,834 | 3,256 | +15% | 0 | 0 | — |
case-08 | pass→pass | 16,040 | 10,236 | -36% | 1 | 1 | 0% | 1,705 | 1,902 | +12% | 0 | 0 | — |
case-09 | pass→pass | 3,790 | 9,100 | +140% | 1 | 1 | 0% | 701 | 1,538 | +119% | 0 | 0 | — |
case-10 | pass→pass | 16,646 | 11,470 | -31% | 1 | 1 | 0% | 1,717 | 2,127 | +24% | 0 | 0 | — |
case-11 | fail→pass | 14,198 | 9,704 | -32% | 1 | 1 | 0% | 1,470 | 1,965 | +34% | 0 | 0 | — |
case-12 | pass→pass | 16,370 | 10,499 | -36% | 1 | 1 | 0% | 1,845 | 2,118 | +15% | 0 | 0 | — |
case-13 | pass→pass | 13,408 | 18,319 | +37% | 1 | 1 | 0% | 2,136 | 2,137 | +0% | 0 | 0 | — |
case-14 | pass→pass | 11,155 | 5,791 | -48% | 1 | 1 | 0% | 985 | 2,098 | +113% | 0 | 0 | — |
case-15 | pass→pass | 13,146 | 10,687 | -19% | 1 | 1 | 0% | 1,453 | 2,209 | +52% | 0 | 0 | — |
case-16 | fail→pass | 10,365 | 12,836 | +24% | 1 | 1 | 0% | 1,632 | 2,476 | +52% | 0 | 0 | — |
case-17 | fail→pass | 7,904 | 9,199 | +16% | 1 | 1 | 0% | 1,277 | 1,915 | +50% | 0 | 0 | — |
case-18 | fail→pass | 14,808 | 2,066 | -86% | 1 | 1 | 0% | 2,313 | 1,453 | -37% | 0 | 0 | — |
case-19 | fail→fail | 18,460 | 12,620 | -32% | 1 | 1 | 0% | 1,121 | 1,543 | +38% | 0 | 0 | — |
case-20 | fail→fail | 10,498 | 10,558 | +1% | 1 | 1 | 0% | 236 | 1,382 | +486% | 0 | 0 | — |
case-21 | fail→fail | 17,101 | 12,660 | -26% | 1 | 1 | 0% | 341 | 1,828 | +436% | 0 | 0 | — |
case-22 | fail→fail | 5,934 | 11,296 | +90% | 1 | 1 | 0% | 899 | 1,421 | +58% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 13 counted toward the lift figure. The other 9 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +23 percentage points is the difference between those two pass rates over the 13 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.