by-session

Name: by-session
Author: 001TMF

Other Verified v1.0

Repo stars 80
Author updated Live
Author repo blatant-why

Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network

Domain: Other
Compatible agents: Claude Code
Cursor
Cline
Codex
Windsurf
Gemini CLI

+20
Trust score: 92 / 100 · audit passed
Author / version / license: @001TMF · v1.0 · no license declared
Token usage: Lean
Setup complexity: Manual integration
External API key: Required · Vendor-specific
Operating systems: macOS · Linux · Windows
Runtime requirements: Node.js · Python >=3.10
Permissions: Read-only
Write / modify
Shell exec
Env read
Network behavior: External requests
Install commands: 26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview by-session.preview

---
name: by-session
description: Session initialization and configuration for BY projects. This skill defines the full session-st…
category: other
runtime: Node.js / Python
---

# by-session output preview

## PART A: Task fit
- Use case: Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner, environment check, status display) and the first-run configuration questionnaire that writes .by/config.json. It is not optional — it requires Vendor-specific API key; runs on Node.js. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use This Skill / Quick Start / Inputs” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner, environment check, status display) and the first-run configuration questionnaire that writes .by/config.json. It is not optional — it requires Vendor-specific API key; runs on Node.js. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “When to Use This Skill / Quick Start / Inputs” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.

## Running Rules
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.

SKILL.md SKILL.md

---
name: by-session
description: Session initialization and configuration for BY projects. This skill defines the full session-st…
category: other
source: 001TMF/blatant-why
---

# by-session

## When to use
- Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.

## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.

## Execution rules
- Organize steps around “When to Use This Skill / Quick Start / Inputs” and keep inference separate from source facts.
- read files, write/modify files, run shell commands, read environment variables; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.

## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.

Evidence boundary and execution chain

The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.

skill "by-session" {
  input      -> user goal + target files + boundaries + acceptance criteria
  context    -> When to Use This Skill / Quick Start / Inputs
  rules      -> SKILL.md triggers / order / output contract
  runtime    -> Node.js / Python | read files, write/modify files, run shell commands, read environment variables | may access external network resources
  guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
  output     -> copyable result + checklist + next iteration
}

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner, environmen…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “When to Use This Skill”, “Quick Start”, “Inputs”, “Outputs”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name by-session directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “When to Use This Skill / Quick Start / Inputs” before expanding.

Boundaries And Review

Dependencies: Prepare Vendor-specific API keys before running a full task.
Permissions: Declared permissions include read / write / shell-exec / env-read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

BY Session Skill

Session initialization and configuration for BY projects. This skill defines the full session-start sequence (banner, environment check, status display) and the first-run configuration questionnaire that writes .by/config.json. It is not optional — it runs every time a new session opens in a BY project directory.

The session skill is the single source of truth for which compute provider BY will use this session. Every downstream skill (by-design-workflow, by-screening, the design engines) reads .by/config.json written here.

When to Use This Skill

Use this skill when you have:

✅ A new session opening in a BY project directory — the SessionStart hook fires this skill automatically
✅ No .by/config.json yet — run the first-run questionnaire to capture compute provider, model profile, campaign defaults
✅ A request to refresh environment discovery — re-scan local tools, conda envs, GPU, API keys
✅ A request to switch compute provider — update compute.default_provider cleanly without overwriting unrelated fields
✅ An in-progress campaign to detect and surface (campaigns/*/campaign_log.json with status not complete)
✅ Staleness checks — .by/environment.json older than 24h should prompt a refresh

Do NOT use this skill when:

❌ You are mid-campaign and just want status → use the status skill (it reads but does not re-questionnaire)
❌ You need to deploy compute on RunPod / HPC → use by-deploy-compute (this skill only records the choice of HPC)
❌ You are scoring designs or running design engines → use by-scoring, boltzgen, pxdesign, or protenix
❌ You want to inspect a specific campaign's state → use by-campaign-manager
❌ The user is editing .by/config.json by hand → read it back with validate_config.py, do not re-run the questionnaire

Quick Start

User opens a new terminal in a BY project directory.

Agent (by-session):
  1. Banner    → BY ► Protein Design Agent
  2. Check     → .by/config.json exists?
  3a. NO       → run init_questionnaire.py (or its inline AskUserQuestion equivalent)
                 → writes .by/config.json with local-first defaults
  3b. YES      → read config + environment, no questions asked
  4. Resume    → check campaigns/*/campaign_log.json for in-progress work
  5. Status    → print compute + profile + campaign count
  6. Done      → "Ready" prompt with suggested actions

Total time: ~1 second when config exists; ~30 seconds for first-run questionnaire.

Inputs

Required:

Working directory — the BY project root (current directory by default).
User input (first run only) — answers to the 3-round questionnaire (compute provider, model profile, campaign defaults).

Optional (auto-detected):

.by/config.json — if present, skip questionnaire entirely.
.by/environment.json — written by /by:setup; used to confirm tools and GPU.
Environment variables — RUNPOD_API_KEY, TAMARIND_API_KEY, etc. for provider detection.
campaigns/*/campaign_log.json — used to surface in-progress campaigns.

See references/config-schema.md for the full schema of both JSON files.

Outputs

All outputs land under .by/ in the project root.

File	Type	When written	Purpose
`.by/config.json`	JSON	First run, or on user-driven update	User preferences: compute provider, providers_priority, model_profile, campaign defaults
`.by/environment.json`	JSON	First run + `/by:setup` refresh	Auto-detected hardware/tools snapshot (GPU model, VRAM, conda envs, tool paths, API keys present)
Terminal banner	Text	Every session	Branded session header
Status block	Text	Every session	Compute provider in use, profile, campaign count, next-step prompts

The .by/config.json schema is canonical and downstream-critical: every BY skill that selects a compute provider reads this file. See references/config-schema.md.

Clarification Questions

⚠️ CRITICAL: ASK THIS FIRST — Confirm .by/config.json does not already exist before running the questionnaire. Re-running the questionnaire would overwrite the user's saved preferences.

Config file presence (ASK THIS FIRST) — Does .by/config.json already exist? If yes, skip the questionnaire and go straight to the session banner. If no, proceed to Q2. (You can check this silently — only ask the user if there's ambiguity, e.g., the file exists but is corrupted.)
Compute provider — Where should BY run design computations? Options: local (recommended default — uses local GPU), hpc (RunPod or other HPC target deployed via by-deploy-compute), tamarind (Tamarind Bio cloud), auto (detect best available in priority order). The default is local.
Local GPU tool paths (only if provider is local or auto) — Where are BoltzGen, Protenix, and PXDesign installed? Auto-detect from PATH and common locations, or accept a custom path per tool. Validate each path exists.
HPC target (only if provider is hpc) — Which HPC target? Default runpod. The actual deployment is handled by by-deploy-compute; this skill only records the choice and the api_key_env variable name.
Tamarind API key (only if provider is tamarind) — Is TAMARIND_API_KEY set in .env or the shell environment? If missing, point the user to https://tamarind.bio for a free key before continuing.
Model profile — Which AI model profile for sub-agents? balanced (Sonnet, recommended), quality (Opus for research/design agents), budget (Haiku where possible).
Default campaign tier — Default designs per campaign? preview (~~500, fast feasibility checks), standard (~~5,000, recommended), production (~20,000, thorough coverage).

See references/config-schema.md for the exact field names each answer maps to.

Standard Workflow

🚨 MANDATORY: USE THE TWO PROVIDED SCRIPTS — DO NOT WRITE INLINE PYTHON 🚨

This skill ships two scripts that codify the questionnaire and validation logic:

scripts/init_questionnaire.py — interactive or --defaults first-run setup; writes .by/config.json.
scripts/validate_config.py — reads .by/config.json, validates against the documented schema, prints issues.

When a script does not fit (e.g., the user wants to flip a single field), edit the JSON directly and re-run validate_config.py to confirm correctness. Do not invent a new questionnaire flow inline.

Step 1 — Detect first-run vs returning session

test -f .by/config.json && echo "returning" || echo "first-run"

✅ VERIFICATION: Output is exactly returning or first-run.

Step 2a — First-run: run the questionnaire

python3 .claude/skills/by-session/scripts/init_questionnaire.py

Or for a non-interactive default install (CI, automated scaffolding):

python3 .claude/skills/by-session/scripts/init_questionnaire.py --defaults

✅ VERIFICATION: Expect ✓ Wrote .by/config.json with provider=local (or whichever provider the user selected).

The questionnaire collects answers in three rounds (compute, profile, campaign defaults) and writes .by/config.json. The default provider is local and providers_priority is ["local", "hpc", "tamarind"]. If the user picks hpc, the skill records the HPC target (default runpod) and the env var name for the API key, but does NOT deploy anything — deployment lives in by-deploy-compute.

Step 2b — Returning session: read config + environment

python3 .claude/skills/by-session/scripts/validate_config.py .by/config.json

✅ VERIFICATION: Expect ✓ Config valid: provider=<name>, profile=<name>.

If validation fails, surface the specific field error to the user and ask before overwriting anything. Do NOT silently re-run the questionnaire.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 BY ► Protein Design Agent
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Step 4 — Check for in-progress campaigns

ls campaigns/*/campaign_log.json 2>/dev/null

Count campaigns and inspect each status field. See references/resume-protocol.md for the exact rules on what counts as "in progress" and what gets restored.

Step 5 — Display status

Build the compute line from compute.default_provider and any populated provider blocks. Examples:

Compute: Local GPU ✓ (BoltzGen, Protenix, PXDesign)
Compute: HPC ✓ (target=runpod, key=RUNPOD_API_KEY)
Compute: Tamarind ✓ (free tier)

Full display:

Compute: Local GPU ✓ (BoltzGen, Protenix, PXDesign)
Profile: balanced | Default: standard (5,000/scaffold)
Campaigns: 3 previous, 1 active (anti-HER2 — designing 80%)

Ready:
  "Design [modality] against [target]"
  /by:plan-campaign — guided setup
  /by:status — existing campaigns

Anti-patterns

⚠️ CRITICAL — DO NOT:

❌ Re-run the questionnaire if .by/config.json already exists → STOP: read it instead
❌ Default to tamarind → the new default is local; respect providers_priority
❌ Silently overwrite unrelated fields in config.json → load, mutate only target field, save
❌ Block the session on a single failed tool check → mark ✗ with reason, continue
❌ Deploy HPC infrastructure here → that is by-deploy-compute's job

When Scripts Fail

Script-failure hierarchy (per the BY quality bar):

Fix and Retry (90%) — python3 -m pip install -e . may be needed if the project venv is stale; or set PYTHONPATH to include the templates dir. Re-run the script.
Modify Script (5%) — If the questionnaire flow is missing a new option (e.g., a new HPC target), add a branch to init_questionnaire.py. Keep validation in validate_config.py in sync.
Use as Reference (4%) — If the script's behavior is wrong for this user's edge case (e.g., heterogeneous multi-node setup), read the script and adapt the writes manually with python -c "import json; ...".
Write from Scratch (1%) — Only if config.json semantics have changed enough that the script is misleading; in that case, update references/config-schema.md first, then the script.

Decision	Action
Missing `python3`	Step 1 — install Python 3.10+
Missing `argparse` (impossible: stdlib)	Step 1 — Python is broken; reinstall
User wants to keep current config but switch one field	Step 3 — `python3 -c "import json,pathlib; p=pathlib.Path('.by/config.json'); c=json.loads(p.read_text()); c['compute']['default_provider']='hpc'; p.write_text(json.dumps(c, indent=2))"`
Script crashes on malformed config	Step 2 — patch `validate_config.py` to surface a friendlier error

Decision Points

Default provider selection

Situation	Recommended provider	Why
User has a local NVIDIA GPU with ≥ 24 GB VRAM	`local`	Fastest, no cloud cost; matches new default
User has a local GPU but with < 24 GB VRAM	`local` for inference, `hpc` for design	BoltzGen design is memory-heavy
User has no local GPU, has RunPod credits	`hpc` (target=runpod)	Predictable cost, full GPU control via `by-deploy-compute`
User has no local GPU, no HPC credits	`tamarind`	Free tier covers small campaigns
User is unsure	`auto`	Probes in order `local → hpc → tamarind`

Model profile selection

Profile	Sub-agent models	When to choose
`balanced`	Sonnet for most agents	Default — good quality/cost ratio
`quality`	Opus for research + design	Novel target, high-stakes campaign
`budget`	Haiku where possible	Cost-constrained, well-studied target

Campaign tier default

Tier	Designs/scaffold	When to default to it
`preview`	~500	Tutorials, feasibility checks
`standard`	~5,000	Recommended; most campaigns
`production`	~20,000	Hard targets, last-mile screening

Common Issues

Issue	Cause	Solution
`.by/config.json` exists but `default_provider` is `tamarind`	Pre-2026-05 config — Tamarind used to be the default	Run `validate_config.py --suggest-fix` to surface; offer to flip to `local`
Questionnaire re-runs every session	`.by/config.json` is being deleted by a hook or `.gitignore` exclusion	Confirm `.by/` is committed (or at least persisted between sessions); inspect `.git/info/exclude` and `.gitignore`
`validate_config.py` reports `providers_priority` missing	Config predates the local-first migration	Run `init_questionnaire.py --defaults --keep-existing` to merge in the new field without nuking other settings
`compute.hpc.target` is empty after picking `hpc`	User skipped Q4	Re-ask Q4; default is `runpod`
`RUNPOD_API_KEY` env var unset but provider is `hpc`	API key not exported in `.env`	Point user to `by-deploy-compute` for setup; do NOT block the session
`TAMARIND_API_KEY` env var unset but provider is `tamarind`	Same as above for Tamarind	Show the signup URL; mark provider `✗` in status until set
Local GPU tool paths point to nonexistent dirs	Tools moved or were never installed	Mark each tool `✗ <reason>` in the status block; do NOT change config without asking
Banner prints garbled box-drawing characters	Terminal not UTF-8	Document the limitation; suggest `export LANG=en_US.UTF-8`
`environment.json` is older than 24h	User ran `/by:setup` long ago; tools may have changed	Suggest `/by:setup` to refresh; do NOT auto-refresh without consent
Multiple `.by/` dirs in nested project roots	Monorepo with sub-projects	Use the closest `.by/` walking up from `cwd`; document the choice in the status block
In-progress campaign was force-killed and `campaign_log.json` says `running`	Stale lock	See `references/resume-protocol.md` — surface to user, ask whether to resume or mark complete
Sub-agent receives the wrong compute provider	Sub-agent did not read `.by/config.json`	Always include the compute config explicitly in `Task()` prompts; this skill cannot enforce that downstream

Best Practices

🚨 CRITICAL: Always check .by/config.json existence BEFORE running the questionnaire — re-running silently overwrites user preferences.
✅ REQUIRED: Use scripts/init_questionnaire.py and scripts/validate_config.py — do not write inline Python for these flows.
✅ Honor providers_priority (["local", "hpc", "tamarind"]) when default_provider is auto.
✅ When updating config, read-modify-write — never replace the file wholesale unless the user opts in.
✅ Surface compute-provider issues but do NOT block the session — the user may want to fix them mid-session.
✅ Treat .by/environment.json as advisory; the config is canonical for "which provider to use".
❌ Never expose API key VALUES in the banner or logs — only present/missing.
❌ Never silently fall back from local to tamarind — respect the user's explicit choice.
✨ Optional: Cache the questionnaire answers in .by/.questionnaire-history.json if the user re-runs setup, so we can suggest their previous choices as defaults.

Suggested Next Steps

After the session is initialized, common next moves:

by-design-workflow — when the user describes a target, this is the master orchestration skill that decides which design engine to invoke (it reads the compute provider from .by/config.json written here).
by-research — if the user is starting a new campaign against an unfamiliar target, research is always step 1.
by-campaign-manager — if the session detected an in-progress campaign, hand off to this skill for resume.
by-deploy-compute — if the user selected hpc but the actual deployment is not yet active.
/by:status — if the user just wants a campaign-status summary without starting work.

The chaining matters: by-session's job is to prepare the environment; downstream skills consume .by/config.json and trust it. Keep that contract clean.

Upstream (run before by-session): None — this skill is the entry point.

Downstream (run after by-session):

by-design-workflow — master orchestration; reads provider from config
by-research — first step of any new campaign
by-campaign-manager — for resume of in-progress campaigns

Alternative / Complementary:

by-deploy-compute — handles the deployment side of HPC; by-session only records the choice
status (slash command skill) — read-only campaign status; does not modify config

References

Detailed documentation (references/):

references/config-schema.md — Full JSON Schema for .by/config.json and .by/environment.json, including the post-2026-05 fields (providers_priority, compute.hpc.target, compute.hpc.api_key_env).
references/resume-protocol.md — Detection rules for in-progress campaigns, checkpoint loading, and what is restored vs re-asked when a session reopens mid-campaign.

Scripts (scripts/):

scripts/init_questionnaire.py — Interactive (or --defaults) first-run config flow. Asks: local GPU available? RunPod API key? Tamarind key? Writes .by/config.json with local-first defaults.
scripts/validate_config.py — Reads .by/config.json, validates against the schema documented in references/config-schema.md, prints actionable issues.

Project context:

templates/CLAUDE.md — the "Compute Provider Selection" section is the authoritative prose explanation of the local-first ordering this skill enforces.
templates/.by/config.json — the canonical default config shipped with new projects.

Install command

One-click installPick a runner, click Copy, paste into your terminal, press enter

npx skills add 001TMF/blatant-why/templates/.claude/skills/by-session -y

Open general install guide

View skill source

Works with

Claude Code
Cursor
Cline
Codex
Windsurf
Gemini
OpenCode
Roo Code
AAide
+Augment
GitHub Copilot
OpenClaw
KKiro
Junie
WWarp
Goose
Trae
Kilo Code
OpenHands
Amp
Zencoder
CContinue
AIAider
CodeBuddy
CodeGeeX
Replit Agent

Security audit

92 / 100

Verified

未声明 license/许可。
未限定 allowed-tools，默认拥有全部工具权限。

Metadata

Source: github
001TMF/blatant-why
Version: 1.0
中文

More by @001TMF

Category & tags

Category Other

by-session

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

BY Session Skill

When to Use This Skill

Quick Start

Inputs

Outputs

Clarification Questions

Standard Workflow

Step 1 — Detect first-run vs returning session

Step 2a — First-run: run the questionnaire

Step 2b — Returning session: read config + environment

Step 3 — Show banner

Step 4 — Check for in-progress campaigns

Step 5 — Display status

Anti-patterns

When Scripts Fail

Decision Points

Default provider selection

Model profile selection

Campaign tier default

Common Issues

Best Practices

Suggested Next Steps

References

Discussion

Decide Fit First

Design Intent

How To Use It

Boundaries And Review

BY Session Skill

When to Use This Skill

Quick Start

Inputs

Outputs

Clarification Questions

Standard Workflow

Step 1 — Detect first-run vs returning session

Step 2a — First-run: run the questionnaire

Step 2b — Returning session: read config + environment

Step 3 — Show banner

Step 4 — Check for in-progress campaigns

Step 5 — Display status

Anti-patterns

When Scripts Fail

Decision Points

Default provider selection

Model profile selection

Campaign tier default

Common Issues

Best Practices

Suggested Next Steps

Related Skills

References

Discussion