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chore(#604): rename get-shit-done/ runtime directory to gsd-core/ (#615)

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* chore(#604): rename get-shit-done/ runtime directory to gsd-core/

Renames the installed runtime directory `get-shit-done/` to `gsd-core/` so the
on-disk name matches the package (`@opengsd/gsd-core`), repo, and binary
(`gsd-tools`). The npm package name and binary are unchanged; npx/npm consumers
are unaffected.

Mechanical (bulk, ~90% of the diff):
- `git mv get-shit-done gsd-core`
- Swept path/identifier references across the repo via
  `perl -pe 's/get-shit-done(?!-\w)/gsd-core/g'`. The negative lookahead
  preserves the five legitimate slug variants that are NOT the directory:
  get-shit-done-{OLD,cc,classic,cli,redux} (old package/repo names).
- Build/manifest wiring: package.json (bin, files, coverage globs),
  tsconfig.build.json (outDir), ~86 .gitignore build-output entries,
  stryker.config.mjs, scan-ignore files, install.js path strings.
- Frozen (not rewritten): CHANGELOG.md history; translated docs
  (README.<locale>.md and docs/{ja-JP,ko-KR,pt-BR,zh-CN}/).

New logic (review here):
- src/installer-migrations/003-rename-get-shit-done-to-gsd-core.cts: a proper
  ADR-0008 installer migration. On upgrade it walks the legacy
  `~/.claude/get-shit-done/` tree, classifies each file via the prior install
  manifest, and emits remove-managed / backup-and-remove for managed files
  while PRESERVING unknown user-added files. Symlink-safe (skips a symlinked
  root and symlinked entries; bounds-checks every path under configDir). The
  framework rolls back on install failure. Emptied dirs may remain (framework
  has no recursive dir-removal primitive) — documented.
- scripts/lint-legacy-dir-name.cjs: CI regression guard forbidding the bare
  `get-shit-done` directory token (split token to avoid self-match; case-
  insensitive; `(?!-\w)` lookahead allows the slug variants; allowlists
  CHANGELOG, translated docs, and `gsd-allow-legacy-name` marker lines).
  Wired into the lint-tests CI job.
- Restored scripts/lint-package-identity-drift.cjs detection regexes (the
  mechanical sweep had wrongly rewritten the old-name patterns it exists to
  detect) and marked them as intentional legacy references.
- TDD tests for the migration and the guard; do.md slash-command guard regex
  tightened so a `/gsd-core/bin` path segment is not mistaken for a command;
  changeset + docs/installer-migrations.md row added.

Breaking: the installed runtime path moves `~/.claude/get-shit-done/` ->
`~/.claude/gsd-core/`. Migration 003 removes the stale legacy dir's managed
files (preserving user files) on upgrade. Users with custom hooks/configs
hardcoding the old path must update them.

Closes #604

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): unsweep pending changesets + allowlist injection-example docs

CI fixes for the rename PR:
- Do not sweep pending .changeset/*.md (ephemeral release-note fragments,
  like CHANGELOG); reverted those body edits so 5 pre-existing malformed
  fragments (missing type/pr) no longer enter the PR diff and trip docs-lint.
  Allowlisted .changeset/ in the legacy-name guard accordingly.
- Allowlisted TEST-EXAMPLES.md and docs/explanation/security-model.md in
  prompt-injection-scan.sh: they contain intentional injection examples /
  security-model prose; the path-reference rewrites are kept.

CodeQL alerts on this PR are pre-existing (alert lines unchanged by this PR;
none in the new migration/guard) and are out of scope for the rename.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): resolve CodeQL alerts surfaced on this PR

The rename diff touched files carrying pre-existing CodeQL findings; per the
no-pre-existing-dismissal rule, fixing every surfaced alert rather than waving
them off. All behavior-preserving:

- scripts/ci-test-scope.cjs: build the config-path match from string
  .includes() instead of a RegExp over an arg-derived value (js/regex-injection).
- src/profile-output.cts: escape backslashes before pipe-escaping desc/safeName
  so the table-cell escape is complete (js/incomplete-sanitization).
- tests/{bug-2643,bug-2808,docs-parity-live-registry}: two-pass HTML-comment
  strip so a bare/unclosed `<!--` cannot survive (js/incomplete-multi-character-sanitization).
- tests/inline-plan-threshold: drop the no-op `\s`->`\s` identity replace,
  keep the meaningful POSIX-class conversion (js/identity-replacement).

Verified: build:lib green; the touched test files + ci-test-scope + profile-output
suites pass; lint:legacy-name clean.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): correctly resolve remaining CodeQL alerts (regex-injection + sanitization)

The prior commit's fixes for two alerts were ineffective:
- ci-test-scope.cjs js/regex-injection: the alert is the CLI-arg-derived `file`
  reaching static regex `.test(file)` calls (not the config rule). Removed ALL
  regex over file/t — startsWith/includes/=== string checks + an isWindowsHint
  helper — so there is no regex sink for the tainted value.
- js/incomplete-multi-character-sanitization (3 test files): a single
  `.replace(/<!--...-->/g,'')` can let `<!--` re-form. Replaced with a fixpoint
  loop (replace until stable) plus a final bare-opener strip.

Verified: no regex over file/t remains; ci-test-scope + the 3 test suites pass;
lint:legacy-name clean.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): make ci-test-scope + comment-strippers regex-free to clear CodeQL

CodeQL flags the regex PATTERNS syntactically (regex-injection on the
--files arg split; incomplete-multi-character-sanitization on the <!--...-->
replace), so loop fixes do not satisfy it. Made these paths regex-free:
- ci-test-scope.cjs splitFiles: char-by-char separator tokenizer (no /[,\\s]+/).
- 3 test files: indexOf/slice HTML-comment stripper (no .replace(/<!--/)).
Behavior preserved; ci-test-scope + the 3 suites pass; guard clean.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): unblock security base64 scan on the large rename diff

The security job hit its 10m timeout: base64-scan.sh choked on the binary
test fixture tests/feat-3594-parser-property-style.test.cjs (embedded NUL/
non-UTF8 bytes -> thousands of bogus blobs + "ignored null byte" warnings),
and the ~800-file rename diff is slow to scan regardless.

- scripts/base64-scan.sh: skip binary-by-content files (grep -Iq .) — they
  can't carry base64-obfuscated *text* and feeding NUL bytes through the
  per-line scanner is pathologically slow. collect_files already filtered
  binary *extensions*; this catches binary *content* in text extensions.
- .github/workflows/security-scan.yml: raise the security job timeout 10m->30m
  to accommodate very large diffs (the scan itself is unchanged).

Verified locally: scan skips the fixture, 0 "ignored null byte" warnings,
0 findings, exit 0.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): sweep get-shit-done refs introduced by merging next

The branch was updated with next (#614/#384/#618 etc.), which reference the
get-shit-done/ dir (still named that on next). Swept the stale references in
the merged files to gsd-core so the rename stays consistent and lint:legacy-name
passes:
- commands/gsd/discuss-phase.md (runtime-launcher shim paths)
- src/core.cts (getAgentsDir layout comments)
- tests/bug-384-agents-runtime-aware.test.cjs (require path to runtime lib)

Verified: guard 0 violations; build green.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): exclude gsd-core/ path segments from bug-3683 command cross-ref invariant

The #614 runtime-launcher shim added to discuss-phase.md references
`${_GSD_RUNTIME_ROOT}/gsd-core/bin/...`. bug-3683's REF_PATTERN excluded path-y
refs only via lookbehind, but `}` precedes `/gsd-core/` in the shim, so it
mis-read the directory path as a dangling `/gsd-core` command ref (same class as
the #604 bug-2954 fix). Added a trailing `(?![\w-]*\/)` so `/gsd-<x>/...` path
segments are not treated as slash-command references.

Verified locally on BOTH platforms before pushing:
- mac (node 26) full suite: 0 failures
- gsd-test-runner (linux, node22 image) full suite: 0 failures
- bug-3683 + bug-2954 pass.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): lazily resolve findProjectRoot in gsd-tools (harden flaky CI)

CI intermittently failed state.test's gsd-tools subprocess with
"findProjectRoot is not a function" (flip-flopping across legs; not reproducible
on mac full suite, gsd-test linux full suite, test:unit, or state.test x8).
findProjectRoot is a re-export from core.cjs (sourced from project-root.cjs);
binding it via destructure at module-load can be undefined under a load-ordering
edge. Resolve it lazily at call time via a small wrapper so the lookup happens
after core.cjs is fully initialized.

Verified green on BOTH platforms before pushing:
- mac (node 26) full suite: 0 failures
- gsd-test-runner (linux, node22) full suite: 0 failures
- state.test.cjs: 106/106; gsd-tools loads cleanly.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(#604): allowlist verification-patterns.md placeholder examples in secret scan

The rename git-mv'd references/verification-patterns.md into gsd-core/, pulling
it into the secret-scan diff. It documents stub/placeholder RED-FLAG env-var
examples (illustrative Stripe test-key / database-URL / API-key placeholders) —
not real credentials. Added it to .secretscanignore with the strict annotation,
mirroring the existing gsd-core/workflows/plan-phase.md exception.

Verified locally: secret-scan-lint --strict OK; secret-scan --diff origin/next
exits 0 with 0 findings.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Tom Boucher
2026-06-02 18:35:29 -04:00
committed by GitHub
parent b177c1704f
commit 463cffd894
845 changed files with 3316 additions and 2373 deletions
Download Patch File Download Diff File Expand all files Collapse all files

186
gsd-core/references/ai-frameworks.md Normal file
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# AI Framework Decision Matrix
> Reference used by `gsd-framework-selector` and `gsd-ai-researcher`.
> Distilled from official docs, benchmarks, and developer reports (2026).
---
## Quick Picks
| Situation | Pick |
|-----------|------|
| Simplest path to a working agent (OpenAI) | OpenAI Agents SDK |
| Simplest path to a working agent (model-agnostic) | CrewAI |
| Production RAG / document Q&A | LlamaIndex |
| Complex stateful workflows with branching | LangGraph |
| Multi-agent teams with defined roles | CrewAI |
| Code-aware autonomous agents (Anthropic) | Claude Agent SDK |
| "I don't know my requirements yet" | LangChain |
| Regulated / audit-trail required | LangGraph |
| Enterprise Microsoft/.NET shops | AutoGen/AG2 |
| Google Cloud / Gemini-committed teams | Google ADK |
| Pure NLP pipelines with explicit control | Haystack |
---
## Framework Profiles
### CrewAI
- **Type:** Multi-agent orchestration
- **Language:** Python only
- **Model support:** Model-agnostic
- **Learning curve:** Beginner (role/task/crew maps to real teams)
- **Best for:** Content pipelines, research automation, business process workflows, rapid prototyping
- **Avoid if:** Fine-grained state management, TypeScript, fault-tolerant checkpointing, complex conditional branching
- **Strengths:** Fastest multi-agent prototyping, 5.76x faster than LangGraph on QA tasks, built-in memory (short/long/entity/contextual), Flows architecture, standalone (no LangChain dep)
- **Weaknesses:** Limited checkpointing, coarse error handling, Python only
- **Eval concerns:** Task decomposition accuracy, inter-agent handoff, goal completion rate, loop detection
### LlamaIndex
- **Type:** RAG and data ingestion
- **Language:** Python + TypeScript
- **Model support:** Model-agnostic
- **Learning curve:** Intermediate
- **Best for:** Legal research, internal knowledge assistants, enterprise document search, any system where retrieval quality is the #1 priority
- **Avoid if:** Primary need is agent orchestration, multi-agent collaboration, or chatbot conversation flow
- **Strengths:** Best-in-class document parsing (LlamaParse), 35% retrieval accuracy improvement, 20-30% faster queries, mixed retrieval strategies (vector + graph + reranker)
- **Weaknesses:** Data framework first — agent orchestration is secondary
- **Eval concerns:** Context faithfulness, hallucination, answer relevance, retrieval precision/recall
### LangChain
- **Type:** General-purpose LLM framework
- **Language:** Python + TypeScript
- **Model support:** Model-agnostic (widest ecosystem)
- **Learning curve:** Intermediate–Advanced
- **Best for:** Evolving requirements, many third-party integrations, teams wanting one framework for everything, RAG + agents + chains
- **Avoid if:** Simple well-defined use case, RAG-primary (use LlamaIndex), complex stateful workflows (use LangGraph), performance at scale is critical
- **Strengths:** Largest community and integration ecosystem, 25% faster development vs scratch, covers RAG/agents/chains/memory
- **Weaknesses:** Abstraction overhead, p99 latency degrades under load, complexity creep risk
- **Eval concerns:** End-to-end task completion, chain correctness, retrieval quality
### LangGraph
- **Type:** Stateful agent workflows (graph-based)
- **Language:** Python + TypeScript (full parity)
- **Model support:** Model-agnostic (inherits LangChain integrations)
- **Learning curve:** Intermediate–Advanced (graph mental model)
- **Best for:** Production-grade stateful workflows, regulated industries, audit trails, human-in-the-loop flows, fault-tolerant multi-step agents
- **Avoid if:** Simple chatbot, purely linear workflow, rapid prototyping
- **Strengths:** Best checkpointing (every node), time-travel debugging, native Postgres/Redis persistence, streaming support, chosen by 62% of developers for stateful agent work (2026)
- **Weaknesses:** More upfront scaffolding, steeper curve, overkill for simple cases
- **Eval concerns:** State transition correctness, goal completion rate, tool use accuracy, safety guardrails
### OpenAI Agents SDK
- **Type:** Native OpenAI agent framework
- **Language:** Python + TypeScript
- **Model support:** Optimized for OpenAI (supports 100+ via Chat Completions compatibility)
- **Learning curve:** Beginner (4 primitives: Agents, Handoffs, Guardrails, Tracing)
- **Best for:** OpenAI-committed teams, rapid agent prototyping, voice agents (gpt-realtime), teams wanting visual builder (AgentKit)
- **Avoid if:** Model flexibility needed, complex multi-agent collaboration, persistent state management required, vendor lock-in concern
- **Strengths:** Simplest mental model, built-in tracing and guardrails, Handoffs for agent delegation, Realtime Agents for voice
- **Weaknesses:** OpenAI vendor lock-in, no built-in persistent state, younger ecosystem
- **Eval concerns:** Instruction following, safety guardrails, escalation accuracy, tone consistency
### Claude Agent SDK (Anthropic)
- **Type:** Code-aware autonomous agent framework
- **Language:** Python + TypeScript
- **Model support:** Claude models only
- **Learning curve:** Intermediate (18 hook events, MCP, tool decorators)
- **Best for:** Developer tooling, code generation/review agents, autonomous coding assistants, MCP-heavy architectures, safety-critical applications
- **Avoid if:** Model flexibility needed, stable/mature API required, use case unrelated to code/tool-use
- **Strengths:** Deepest MCP integration, built-in filesystem/shell access, 18 lifecycle hooks, automatic context compaction, extended thinking, safety-first design
- **Weaknesses:** Claude-only vendor lock-in, newer/evolving API, smaller community
- **Eval concerns:** Tool use correctness, safety, code quality, instruction following
### AutoGen / AG2 / Microsoft Agent Framework
- **Type:** Multi-agent conversational framework
- **Language:** Python (AG2), Python + .NET (Microsoft Agent Framework)
- **Model support:** Model-agnostic
- **Learning curve:** Intermediate–Advanced
- **Best for:** Research applications, conversational problem-solving, code generation + execution loops, Microsoft/.NET shops
- **Avoid if:** You want ecosystem stability, deterministic workflows, or "safest long-term bet" (fragmentation risk)
- **Strengths:** Most sophisticated conversational agent patterns, code generation + execution loop, async event-driven (v0.4+), cross-language interop (Microsoft Agent Framework)
- **Weaknesses:** Ecosystem fragmented (AutoGen maintenance mode, AG2 fork, Microsoft Agent Framework preview) — genuine long-term risk
- **Eval concerns:** Conversation goal completion, consensus quality, code execution correctness
### Google ADK (Agent Development Kit)
- **Type:** Multi-agent orchestration framework
- **Language:** Python + Java
- **Model support:** Optimized for Gemini; supports other models via LiteLLM
- **Learning curve:** Intermediate (agent/tool/session model, familiar if you know LangGraph)
- **Best for:** Google Cloud / Vertex AI shops, multi-agent workflows needing built-in session management and memory, teams already committed to Gemini, agent pipelines that need Google Search / BigQuery tool integration
- **Avoid if:** Model flexibility is required beyond Gemini, no Google Cloud dependency acceptable, TypeScript-only stack
- **Strengths:** First-party Google support, built-in session/memory/artifact management, tight Vertex AI and Google Search integration, own eval framework (RAGAS-compatible), multi-agent by design (sequential, parallel, loop patterns), Java SDK for enterprise teams
- **Weaknesses:** Gemini vendor lock-in in practice, younger community than LangChain/LlamaIndex, less third-party integration depth
- **Eval concerns:** Multi-agent task decomposition, tool use correctness, session state consistency, goal completion rate
### Haystack
- **Type:** NLP pipeline framework
- **Language:** Python
- **Model support:** Model-agnostic
- **Learning curve:** Intermediate
- **Best for:** Explicit, auditable NLP pipelines, document processing with fine-grained control, enterprise search, regulated industries needing transparency
- **Avoid if:** Rapid prototyping, multi-agent workflows, or you want a large community
- **Strengths:** Explicit pipeline control, strong for structured data pipelines, good documentation
- **Weaknesses:** Smaller community, less agent-oriented than alternatives
- **Eval concerns:** Extraction accuracy, pipeline output validity, retrieval quality
---
## Decision Dimensions
### By System Type
| System Type | Primary Framework(s) | Key Eval Concerns |
|-------------|---------------------|-------------------|
| RAG / Knowledge Q&A | LlamaIndex, LangChain | Context faithfulness, hallucination, retrieval precision/recall |
| Multi-agent orchestration | CrewAI, LangGraph, Google ADK | Task decomposition, handoff quality, goal completion |
| Conversational assistants | OpenAI Agents SDK, Claude Agent SDK | Tone, safety, instruction following, escalation |
| Structured data extraction | LangChain, LlamaIndex | Schema compliance, extraction accuracy |
| Autonomous task agents | LangGraph, OpenAI Agents SDK | Safety guardrails, tool correctness, cost adherence |
| Content generation | Claude Agent SDK, OpenAI Agents SDK | Brand voice, factual accuracy, tone |
| Code automation | Claude Agent SDK | Code correctness, safety, test pass rate |
### By Team Size and Stage
| Context | Recommendation |
|---------|----------------|
| Solo dev, prototyping | OpenAI Agents SDK or CrewAI (fastest to running) |
| Solo dev, RAG | LlamaIndex (batteries included) |
| Team, production, stateful | LangGraph (best fault tolerance) |
| Team, evolving requirements | LangChain (broadest escape hatches) |
| Team, multi-agent | CrewAI (simplest role abstraction) |
| Enterprise, .NET | AutoGen AG2 / Microsoft Agent Framework |
### By Model Commitment
| Preference | Framework |
|-----------|-----------|
| OpenAI-only | OpenAI Agents SDK |
| Anthropic/Claude-only | Claude Agent SDK |
| Google/Gemini-committed | Google ADK |
| Model-agnostic (full flexibility) | LangChain, LlamaIndex, CrewAI, LangGraph, Haystack |
---
## Anti-Patterns
1. **Using LangChain for simple chatbots** — Direct SDK call is less code, faster, and easier to debug
2. **Using CrewAI for complex stateful workflows** — Checkpointing gaps will bite you in production
3. **Using OpenAI Agents SDK with non-OpenAI models** — Loses the integration benefits you chose it for
4. **Using LlamaIndex as a multi-agent framework** — It can do agents, but that's not its strength
5. **Defaulting to LangChain without evaluating alternatives** — "Everyone uses it" ≠ right for your use case
6. **Starting a new project on AutoGen (not AG2)** — AutoGen is in maintenance mode; use AG2 or wait for Microsoft Agent Framework GA
7. **Choosing LangGraph for simple linear flows** — The graph overhead is not worth it; use LangChain chains instead
8. **Ignoring vendor lock-in** — Provider-native SDKs (OpenAI, Claude) trade flexibility for integration depth; decide consciously
---
## Combination Plays (Multi-Framework Stacks)
| Production Pattern | Stack |
|-------------------|-------|
| RAG with observability | LlamaIndex + LangSmith or Langfuse |
| Stateful agent with RAG | LangGraph + LlamaIndex |
| Multi-agent with tracing | CrewAI + Langfuse |
| OpenAI agents with evals | OpenAI Agents SDK + Promptfoo or Braintrust |
| Claude agents with MCP | Claude Agent SDK + LangSmith or Arize Phoenix |