* feat(adapters): multi-harness framework + harness_portability eval dimension
Turn this Claude Code plugin marketplace into a generic agentic-harness
marketplace. Adapters under tools/adapters/ emit harness-native artifacts
for OpenAI Codex CLI, Cursor, OpenCode, and Gemini CLI from a single
Markdown source. Source-of-truth stays under plugins/ — Claude Code is
unchanged.
Framework (tools/adapters/):
- base.py — PluginSource parser, HarnessAdapter ABC, write/mirror helpers
(path-traversal guard, UTF-8-safe), inline-list + block-list + block-scalar
YAML-ish parser, _utf8_safe_cut, _split_inline_list, _normalize_author
- capabilities.py — per-harness capability matrix, TOOL_NAME_MAPS,
MODEL_ALIASES, resolve_model() with explicit warnings
- codex.py — emits .codex/{skills,agents}/ + AGENTS.md (≤150-line
table-of-contents). Fence-aware body splitter, _utf8_safe_cut for
multibyte safety, _yaml_scalar with reserved-word + special-char quoting.
Skill/command name collision detection (and second-order __cmd fallback).
- cursor.py — emits .cursor-plugin/{plugin,marketplace}.json + curated
.cursor/rules/*.mdc. _validate_mdc_frontmatter handles YAML block scalars
(no false positives on colons in description body). _normalize_author
handles dict, npm-style strings, and author lists.
- opencode.py — transpiles agents to .opencode/agents/<id>.md with
mode:subagent + permission: deny-everything-else block (skill/task always
allowed as base capabilities — Claude's implicit defaults).
- gemini.py — emits native skills/, agents/, and commands/ at extension
root (April 2026 spec). Tool-allowlist remapped via TOOL_NAME_MAPS.
CLI + tooling:
- tools/generate.py — unified `make generate HARNESS=<x> [PLUGIN=<y>]`,
with --clean (containment-guarded; case-insensitive on Darwin/Win32),
--prune (orphan removal across all per-harness output trees), --strict
(warnings fail), per-plugin error aggregation, refuses --clean --plugin
(would silently wipe other plugins' artifacts).
- tools/validate_generated.py — structural validation across all four
harness outputs. Codex 8KB cap → error. _extract_permission_block
correctly handles nested permission keys (column-0 only).
- tools/doc_gardener.py — recurring drift detection per OpenAI harness-
engineering principle. STALE_ARTIFACT (info), DEAD_LINK (error),
MARKETPLACE_ORPHAN (error), SKILL_OVER_CODEX_CAP (warning), grouped
output sorted by severity.
plugin-eval (extends existing framework):
- New harness_portability dimension (6% weight, rebalanced from existing
static sub-scores). Surfaces non-portable patterns with concrete
remediation hints: SKILL_OVER_CODEX_CAP, CLAUDE_TOOL_REFS,
CLAUDE_TOOL_PROSE, AGENT_NAME_COLLISION, BARE_MODEL_ALIAS.
- _CAMEL_TOOL_PATTERN requires Claude-tool context (no false positives
on Rust's `Task` etc.). _TOOL_PROSE_PATTERN case-sensitive on tool
names, case-insensitive on the leading article.
- Findings do NOT also feed anti_pattern_penalty (no double-counting).
Documentation:
- Top-level guides: CODEX.md, CURSOR.md, OPENCODE.md (≤150 lines each,
table-of-contents pattern per OpenAI harness-engineering post)
- docs/harnesses.md — capability matrix, graceful-degradation table,
generated output paths
- docs/authoring.md — portable-content style guide (tools, models,
collision rules, fence-respect)
- docs/round-trip-results.md — real-CLI verification recipes (OpenCode
discovers 193 subagents, Gemini extensions validate passes, Codex
TOMLs all parse)
- CONTRIBUTING.md — new file pointing at docs/authoring.md
- README.md — rewritten for multi-harness (145 lines, was 460)
- CLAUDE.md — trimmed to 60-line table-of-contents
- GEMINI.md — trimmed from 1500 to 500 tokens (3× over budget previously)
Tests: 181 passing (103 plugin-eval + 78 tools/tests). Real-CLI round-trip
verified for OpenCode, Gemini, and Codex (TOML parses).
Replaces tools/generate_gemini_commands.py with the unified CLI.
* refactor(skills): extract detail to references/details.md (~75 skills)
Apply Anthropic's canonical SKILL.md progressive-disclosure pattern across
the marketplace: SKILL.md body becomes a navigation tier (trigger phrasing
+ quick start), detailed templates and worked examples move to
references/details.md (loaded on demand by the agent).
Motivation: OpenAI Codex CLI hard-truncates skills at 8 KB. Before this
change, ~90 skills exceeded that cap and would silently break on Codex.
The progressive-disclosure pattern is also Anthropic's documented
recommendation for token efficiency — Claude Code reads references/ files
on demand when the body navigation says to.
What's extracted, by pattern:
- Pass 1 (## Templates section): 19 skills — full template libraries
moved to references/details.md
- Pass 2 (## Implementation Patterns / ## Advanced Patterns): 13 skills
- Pass 3 (everything between nav-tier and wrap-tier headings): 53 skills
- Conservative re-extraction for 8 skills that got over-reduced — kept
~6-7 KB inline (most of the quick-start tier) plus references/ overflow
What stays inline (SKILL.md navigation tier):
- description: frontmatter (triggering — unchanged for all skills)
- ## When to Use This Skill / ## Core Concepts / ## Quick Start
- ## Best Practices / ## Troubleshooting / ## See Also wrap-ups
- A pointer note ("see references/details.md") so the agent knows where
to look for detail
What goes to references/details.md (detail tier, on-demand load):
- ## Templates (full code template libraries)
- ## Implementation Patterns / ## Advanced Patterns (deep examples)
- Mid-skill walkthroughs that exceed the inline budget
Also in this commit:
- plugins/brand-landingpage description trimmed from 958→543 chars
(preserves trigger phrasing, drops verbose example-quote list)
Net effect:
- SKILL_OVER_CODEX_CAP findings: 90 → 10 (88% reduction)
- All triggers unchanged — discovery behavior identical across harnesses
- 75 new references/details.md files with the extracted content
- Same depth of guidance, loaded progressively
Remaining 10 oversized skills are complex multi-section docs (e.g.
postgresql, code-review-excellence, evaluation-methodology) that need
per-skill manual judgment — flagged by `make garden` for future work.
* chore: bump all plugin versions (multi-harness release)
Patch-bump every local plugin (81) in both .claude-plugin/marketplace.json
entries and each plugins/<name>/.claude-plugin/plugin.json. Minor-bump the
top-level marketplace metadata.version (1.6.0 → 1.7.0) to signal the
multi-harness adapter framework addition.
The external git-subdir entry (qa-orchestra) is unaffected — its version
is governed by its upstream repo.
* fix(opencode): preserve explicit tools:[] + word-boundary subtask match
Addresses two Codex review findings on PR #541.
## P1 — `tools: []` silently upgraded to permissive (privilege escalation)
Before: `_build_permission_block` returned `{}` for any empty list, which
omits the `permission:` block entirely from the emitted agent. An author
who explicitly wrote `tools: []` to lock down an advisory-only agent got
an UNRESTRICTED agent in OpenCode. Affected agent in this tree:
`plugins/arm-cortex-microcontrollers/agents/arm-cortex-expert.md`.
Fix: `_build_permission_block` now takes a `has_tools_field` flag so the
caller can distinguish "tools: key missing" (Claude default permissive)
from "tools: []" (explicit lock-down). The lock-down case emits a
deny-everything block that allows ONLY the base capabilities (skill, task)
that Claude Code always grants implicitly. Verified against the real
arm-cortex-expert agent — now emits read/edit/write/bash/grep/glob/list:
deny, task/skill: allow.
## P2 — `"agent" in cmd.body.lower()` false-positives on substrings
Before: a command body containing `PerformanceReviewAgent` (class name
in a code snippet) or `useragent` triggered `subtask: true`, changing
runtime behavior based on incidental text.
Fix: switch to a compiled word-boundary regex `\b(agent|subagent)s?\b`
(case-insensitive). Tests confirm the substring `PerformanceReviewAgent`
no longer fires, while a real "spawn a subagent" sentence still does.
## Tests
3 new regression tests in tools/tests/test_adapters.py:
- `test_explicit_empty_tools_yields_locked_permission_block` (P1)
- `test_missing_tools_field_yields_no_permission_block` (P1 boundary)
- `test_subtask_inference_word_boundary` (P2)
184 total tests pass (was 181). OpenCode round-trip still discovers all
193 subagents; arm-cortex-expert agent is now properly locked down.
* test: behavioral verification + CI gates for multi-harness pipeline
Adds three layers of automated verification that pure-Python parser tests
miss, plus the CI jobs that turn them into hard gates. Catches the kinds
of issues that previously only surfaced when a real user installed the
marketplace and tried to use it.
## test_real_world.py — real-source structural tests
Runs against the actual `plugins/` tree (not synthetic fixtures). Catches
issues that only appear on real content:
- every marketplace entry resolves to a plugins/<name>/ dir
- every local plugin dir appears in marketplace.json
- marketplace.json version == per-plugin plugin.json version (catches drift)
- every plugin loads via load_plugin() without error
- no plugin name contains `__` (adapter namespace separator)
- every agent has name + description; every skill has a trigger phrase
(same regex plugin_eval's MISSING_TRIGGER check uses)
- no agent name collides with Codex built-ins
- every refactored skill (with `references/details.md`) has:
- meaningful detail content (>=500 B in details.md)
- a pointer to references/ in the SKILL.md body
- a navigation-tier heading preserved (When to Use, Overview, etc.)
- body >= 600 B (not a stub)
- every plugin.json has name + version matching the dir
This test pass found and fixed three real defects before commit:
- ship-mate/skills/scan: description had no trigger phrase ("Use when…")
- reverse-engineering/skills/memory-forensics: nav-tier section lost
during extraction
- reverse-engineering/skills/binary-analysis-patterns: same
All three are now fixed (preserved trigger phrasing, added When-to-Use
sections back to the skills my extraction over-trimmed).
## test_round_trip.py — generate→parse→verify
CI runs this AFTER `make generate-all`. Catches generation-time regressions:
- OpenCode/Codex/Gemini agent counts match source agent count (no skips)
- every Codex SKILL.md under 8 KB (the cap that would silently truncate)
- every Codex agent TOML has required fields + valid sandbox_mode
- every OpenCode agent has mode in {primary,subagent,all} and
provider-prefixed model
- locked agents (source `tools: []`) emit proper deny-everything permission
block with skill/task allow (regression guard for PR-541 P1)
- every Gemini @{path} injection resolves to a real source file
- every Gemini command TOML has prompt + {{args}} placeholder
- every context file (CLAUDE.md, AGENTS.md, GEMINI.md, etc.) within
150-line cap
- Cursor marketplace + per-plugin manifests cover all local plugins
- .cursor/rules/*.mdc only use the 3 documented frontmatter keys
## test_cli_smoke.py — real-CLI subprocess tests
Invokes the actual harness binaries (OpenCode, Gemini, Codex, Claude Code)
against the generated artifacts. Catches CLI-level issues pure-Python
parsing can't see: schema-loader drift, plugin-discovery bugs, version
incompatibilities.
- `opencode agent list` — must succeed AND discover every source agent
(currently 191 + 2 OpenCode built-ins)
- `gemini extensions validate <repo>` — must return success
- `codex doctor` — must report healthy install
- every Codex agent TOML must parse with stdlib `tomllib`
- `claude --version` — sanity check the Claude Code CLI loads
- marketplace.json must have owner + metadata.version for Claude Code's loader
Per-CLI tests skip gracefully when the binary isn't on PATH, so local
devs only exercise what they have installed. CI installs OpenCode +
Gemini and turns those skips into hard gates.
## Makefile + CI
- `make test` — full pytest suite (plugin-eval + tools/tests/)
- `make smoke-test` — generates if needed, then runs real-CLI smoke tests
- `.github/workflows/validate.yml` extended with:
- `tools-tests` job — runs pytest tools/tests/
- `multi-harness-generate` job — `make generate-all && make validate
STRICT=1 && make garden`, uploads generated artifacts on every run
- `cli-smoke-test` job — installs OpenCode + Gemini, runs test_cli_smoke.py
## Test counts
- Before: 184 tests
- After: 386 tests (parameterized real-source tests over all 82 plugins)
- All passing locally on OpenCode 1.15.7 + Gemini 0.42.0 + Codex 0.133.0
+ Claude Code 2.1.148
9.8 KiB
context-driven-development — detailed patterns and worked examples
Core Philosophy
Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions.
Key principles:
- Context precedes code: Define what you're building and how before implementation
- Living documentation: Context artifacts evolve with the project
- Single source of truth: One canonical location for each type of information
- AI alignment: Consistent context produces consistent AI behavior
The Workflow
Follow the Context → Spec & Plan → Implement workflow:
- Context Phase: Establish or verify project context artifacts exist and are current
- Specification Phase: Define requirements and acceptance criteria for work units
- Planning Phase: Break specifications into phased, actionable tasks
- Implementation Phase: Execute tasks following established workflow patterns
Artifact Relationships
product.md - Defines WHAT and WHY
Purpose: Captures product vision, goals, target users, and business context.
Contents:
- Product name and one-line description
- Problem statement and solution approach
- Target user personas
- Core features and capabilities
- Success metrics and KPIs
- Product roadmap (high-level)
Update when:
- Product vision or goals change
- New major features are planned
- Target audience shifts
- Business priorities evolve
product-guidelines.md - Defines HOW to Communicate
Purpose: Establishes brand voice, messaging standards, and communication patterns.
Contents:
- Brand voice and tone guidelines
- Terminology and glossary
- Error message conventions
- User-facing copy standards
- Documentation style
Update when:
- Brand guidelines change
- New terminology is introduced
- Communication patterns need refinement
tech-stack.md - Defines WITH WHAT
Purpose: Documents technology choices, dependencies, and architectural decisions.
Contents:
- Primary languages and frameworks
- Key dependencies with versions
- Infrastructure and deployment targets
- Development tools and environment
- Testing frameworks
- Code quality tools
Update when:
- Adding new dependencies
- Upgrading major versions
- Changing infrastructure
- Adopting new tools or patterns
workflow.md - Defines HOW to Work
Purpose: Establishes development practices, quality gates, and team workflows.
Contents:
- Development methodology (TDD, etc.)
- Git workflow and commit conventions
- Code review requirements
- Testing requirements and coverage targets
- Quality assurance gates
- Deployment procedures
Update when:
- Team practices evolve
- Quality standards change
- New workflow patterns are adopted
tracks.md - Tracks WHAT'S HAPPENING
Purpose: Registry of all work units with status and metadata.
Contents:
- Active tracks with current status
- Completed tracks with completion dates
- Track metadata (type, priority, assignee)
- Links to individual track directories
Update when:
- New tracks are created
- Track status changes
- Tracks are completed or archived
See references/artifact-templates.md for copy-paste starter templates.
Context Maintenance Principles
Keep Artifacts Synchronized
Ensure changes in one artifact reflect in related documents:
- New feature in product.md → Update tech-stack.md if new dependencies needed
- Completed track → Update product.md to reflect new capabilities
- Workflow change → Update all affected track plans
Update tech-stack.md When Adding Dependencies
Before adding any new dependency:
- Check if existing dependencies solve the need
- Document the rationale for new dependencies
- Add version constraints
- Note any configuration requirements
Update product.md When Features Complete
After completing a feature track:
- Move feature from "planned" to "implemented" in product.md
- Update any affected success metrics
- Document any scope changes from original plan
Verify Context Before Implementation
Before starting any track:
- Read all context artifacts
- Flag any outdated information
- Propose updates before proceeding
- Confirm context accuracy with stakeholders
Greenfield vs Brownfield Handling
Greenfield Projects (New)
For new projects:
- Run
/conductor:setupto create all artifacts interactively - Answer questions about product vision, tech preferences, and workflow
- Generate initial style guides for chosen languages
- Create empty tracks registry
Characteristics:
- Full control over context structure
- Define standards before code exists
- Establish patterns early
Brownfield Projects (Existing)
For existing codebases:
- Run
/conductor:setupwith existing codebase detection - System analyzes existing code, configs, and documentation
- Pre-populate artifacts based on discovered patterns
- Review and refine generated context
Characteristics:
- Extract implicit context from existing code
- Reconcile existing patterns with desired patterns
- Document technical debt and modernization plans
- Preserve working patterns while establishing standards
Benefits
Team Alignment
- New team members onboard faster with explicit context
- Consistent terminology and conventions across the team
- Shared understanding of product goals and technical decisions
AI Consistency
- AI assistants produce aligned outputs across sessions
- Reduced need to re-explain context in each interaction
- Predictable behavior based on documented standards
Institutional Memory
- Decisions and rationale are preserved
- Context survives team changes
- Historical context informs future decisions
Quality Assurance
- Standards are explicit and verifiable
- Deviations from context are detectable
- Quality gates are documented and enforceable
Directory Structure
conductor/
├── index.md # Navigation hub linking all artifacts
├── product.md # Product vision and goals
├── product-guidelines.md # Communication standards
├── tech-stack.md # Technology preferences
├── workflow.md # Development practices
├── tracks.md # Work unit registry
├── setup_state.json # Resumable setup state
├── code_styleguides/ # Language-specific conventions
│ ├── python.md
│ ├── typescript.md
│ └── ...
└── tracks/
└── <track-id>/
├── spec.md
├── plan.md
├── metadata.json
└── index.md
Context Lifecycle
- Creation: Initial setup via
/conductor:setup - Validation: Verify before each track
- Evolution: Update as project grows
- Synchronization: Keep artifacts aligned
- Archival: Document historical decisions
Context Validation Checklist
Before starting implementation on any track, validate context:
Product Context
- product.md reflects current product vision
- Target users are accurately described
- Feature list is up to date
- Success metrics are defined
Technical Context
- tech-stack.md lists all current dependencies
- Version numbers are accurate
- Infrastructure targets are correct
- Development tools are documented
Workflow Context
- workflow.md describes current practices
- Quality gates are defined
- Coverage targets are specified
- Commit conventions are documented
Track Context
- tracks.md shows all active work
- No stale or abandoned tracks
- Dependencies between tracks are noted
Common Anti-Patterns
Avoid these context management mistakes:
Stale Context
Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process.
Context Sprawl
Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types.
Implicit Context
Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact.
Context Hoarding
Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative.
Over-Specification
Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment.
Integration with Development Tools
IDE Integration
Configure your IDE to display context files prominently:
- Pin conductor/product.md for quick reference
- Add tech-stack.md to project notes
- Create snippets for common patterns from style guides
Git Hooks
Consider pre-commit hooks that:
- Warn when dependencies change without tech-stack.md update
- Remind to update product.md when feature branches merge
- Validate context artifact syntax
CI/CD Integration
Include context validation in pipelines:
- Check tech-stack.md matches actual dependencies
- Verify links in context documents resolve
- Ensure tracks.md status matches git branch state
Session Continuity
Conductor supports multi-session development through context persistence:
Starting a New Session
- Read index.md to orient yourself
- Check tracks.md for active work
- Review relevant track's plan.md for current task
- Verify context artifacts are current
Ending a Session
- Update plan.md with current progress
- Note any blockers or decisions made
- Commit in-progress work with clear status
- Update tracks.md if status changed
Handling Interruptions
If interrupted mid-task:
- Mark task as
[~]with note about stopping point - Commit work-in-progress to feature branch
- Document any uncommitted decisions in plan.md