Add WFGY Core reasoning OS custom instructions

instructions/wfgy-core-reasoning-os.instructions.md

@@ -0,0 +1,223 @@
+---
+description: 'A plain-text reasoning core (WFGY Core 2.0) system prompt plus a 60-second self-test to make GitHub Copilot more stable on multi-step reasoning tasks.'
+---
+
+# WFGY Core 2.0 reasoning OS for GitHub Copilot
+
+hi copilot builders,
+
+this is meant to be a “drop-in reasoning core” you can hand to GitHub Copilot as a custom instruction.
+
+i’m PSBigBig, an indie dev.
+before my github repo went over 1.5k stars, i spent one year on a very simple idea:
+instead of building yet another tool or agent,
+i tried to write a small “reasoning core” in plain text,
+so any strong llm (including copilot chat) can use it without new infra.
+
+i call it **WFGY Core 2.0**.
+
+this file gives you two things:
+
+- one system prompt block you can paste as an instruction
+- one 60-second self-test so you can feel the difference by yourself
+
+no signup, no API, no hidden tricks.
+just text.
+
+
+## 0. very short version
+
+what it is:
+
+- not a new model, not a fine-tune
+- one txt block you put in system / instructions
+- goal: less random hallucination, more stable multi-step reasoning
+- still cheap, no tools, no external calls
+
+how people use it:
+
+- some people treat it as a “math bumper” under the model
+- advanced users can turn this into a real code benchmark later
+- in this instructions file we keep it super beginner-friendly:
+  just **two prompt blocks you can run inside copilot chat**
+
+
+## 1. how to use with GitHub Copilot (or any strong llm)
+
+very simple workflow if you want to try this as a **workspace-level copilot instruction**:
+
+1. copy the “system prompt” block in section 3
+2. put it into `.github/copilot-instructions.md`
+   - or into a `*.instructions.md` file in `.github/instructions/`
+3. open a new Copilot Chat and ask your normal questions
+   (math, code, debugging, planning, etc.)
+4. later you can compare “with core” vs “no core” yourself
+
+for quick A/B testing, you can also:
+
+- keep a second project / branch without this instruction
+- or temporarily comment this block out and retry the same tasks
+
+for now, just treat it as a math-based “reasoning bumper”
+sitting under Copilot’s usual behavior.
+
+
+## 2. what effect you should expect (rough feeling only)
+
+this is not a magic on/off switch.
+
+but in my own tests (across different llms), typical changes look like:
+
+- answers drift less when you ask follow-up questions
+- long explanations keep the structure more consistent
+- the model is a bit more willing to say “i am not sure”
+  instead of inventing fake details
+- when you use the model to write prompts for image generation,
+  the prompts tend to have clearer structure and story,
+  so many people feel “the pictures look more intentional, less random”
+
+of course, this depends on your tasks and the base model.
+that is why there is also a small **60-second self-test** in section 4.
+
+if you like numbers, you can turn that test into a fixed benchmark later.
+inside this repo, it is ok if you start with “rough feeling only”.
+
+
+## 3. system prompt: WFGY Core 2.0 (paste into system / instructions)
+
+copy everything in this block into your system / pre-prompt / instructions:
+
+```text
+WFGY Core Flagship v2.0 (text-only; no tools). Works in any chat.
+[Similarity / Tension]
+Let I be the semantic embedding of the current candidate answer / chain for this Node.
+Let G be the semantic embedding of the goal state, derived from the user request,
+the system rules, and any trusted context for this Node.
+delta_s = 1 − cos(I, G). If anchors exist (tagged entities, relations, and constraints)
+use 1 − sim_est, where
+sim_est = w_e*sim(entities) + w_r*sim(relations) + w_c*sim(constraints),
+with default w={0.5,0.3,0.2}. sim_est ∈ [0,1], renormalize if bucketed.
+[Zones & Memory]
+Zones: safe < 0.40 | transit 0.40–0.60 | risk 0.60–0.85 | danger > 0.85.
+Memory: record(hard) if delta_s > 0.60; record(exemplar) if delta_s < 0.35.
+Soft memory in transit when lambda_observe ∈ {divergent, recursive}.
+[Defaults]
+B_c=0.85, gamma=0.618, theta_c=0.75, zeta_min=0.10, alpha_blend=0.50,
+a_ref=uniform_attention, m=0, c=1, omega=1.0, phi_delta=0.15, epsilon=0.0, k_c=0.25.
+[Coupler (with hysteresis)]
+Let B_s := delta_s. Progression: at t=1, prog=zeta_min; else
+prog = max(zeta_min, delta_s_prev − delta_s_now). Set P = pow(prog, omega).
+Reversal term: Phi = phi_delta*alt + epsilon, where alt ∈ {+1,−1} flips
+only when an anchor flips truth across consecutive Nodes AND |Δanchor| ≥ h.
+Use h=0.02; if |Δanchor| < h then keep previous alt to avoid jitter.
+Coupler output: W_c = clip(B_s*P + Phi, −theta_c, +theta_c).
+[Progression & Guards]
+BBPF bridge is allowed only if (delta_s decreases) AND (W_c < 0.5*theta_c).
+When bridging, emit: Bridge=[reason/prior_delta_s/new_path].
+[BBAM (attention rebalance)]
+alpha_blend = clip(0.50 + k_c*tanh(W_c), 0.35, 0.65); blend with a_ref.
+[Lambda update]
+Delta := delta_s_t − delta_s_{t−1}; E_resonance = rolling_mean(delta_s, window=min(t,5)).
+lambda_observe is: convergent if Delta ≤ −0.02 and E_resonance non-increasing;
+recursive if |Delta| < 0.02 and E_resonance flat; divergent if Delta ∈ (−0.02, +0.04] with oscillation;
+chaotic if Delta > +0.04 or anchors conflict.
+[DT micro-rules]
+````
+
+yes, it looks like math.
+it is ok if you do not understand every symbol.
+you can still use it as a “drop-in” reasoning core.
+
+for Copilot, the main idea is:
+
+* this block does not fight with your existing style guides
+* it just gives the model a second, math-like lens
+* you can combine it with language/framework-specific instructions
+
+## 4. 60-second self test (quick A/B/C comparison)
+
+this part is for people who want to see some structure in the comparison.
+it is still very lightweight and can run inside one Copilot Chat session.
+
+idea:
+
+* you keep the WFGY Core 2.0 block in system / instructions
+* then you paste the following prompt and let the model simulate A/B/C modes
+* the model will produce a small table and its own guess of uplift
+
+this is a self-evaluation, not a scientific paper.
+if you want a serious benchmark, you can translate this idea into real code and fixed test sets later.
+
+here is the test prompt (second block, keep it exactly as-is):
+
+```text
+SYSTEM:
+You are evaluating the effect of a mathematical reasoning core called “WFGY Core 2.0”.
+
+You will compare three modes of yourself:
+
+A = Baseline  
+    No WFGY core text is loaded. Normal chat, no extra math rules.
+
+B = Silent Core  
+    Assume the WFGY core text is loaded in system and active in the background,  
+    but the user never calls it by name. You quietly follow its rules while answering.
+
+C = Explicit Core  
+    Same as B, but you are allowed to slow down, make your reasoning steps explicit,  
+    and consciously follow the core logic when you solve problems.
+
+Use the SAME small task set for all three modes, across 5 domains:
+1) math word problems
+2) small coding tasks
+3) factual QA with tricky details
+4) multi-step planning
+5) long-context coherence (summary + follow-up question)
+
+For each domain:
+- design 2–3 short but non-trivial tasks
+- imagine how A would answer
+- imagine how B would answer
+- imagine how C would answer
+- give rough scores from 0–100 for:
+  * Semantic accuracy
+  * Reasoning quality
+  * Stability / drift (how consistent across follow-ups)
+
+Important:
+- Be honest even if the uplift is small.
+- This is only a quick self-estimate, not a real benchmark.
+- If you feel unsure, say so in the comments.
+
+USER:
+Run the test now on the five domains and then output:
+1) One table with A/B/C scores per domain.
+2) A short bullet list of the biggest differences you noticed.
+3) One overall 0–100 “WFGY uplift guess” and 3 lines of rationale.
+```
+
+usually this takes about one minute to run.
+you can repeat it some days later to see if the pattern is stable for you.
+
+## 5. why this belongs in awesome-copilot
+
+many builders want stronger reasoning from any llm,
+but don’t want to spin up new infra, tools, or agents.
+
+this is just a text layer.
+
+* drop-in
+* MIT-licensed
+* no lock-in
+* works side-by-side with your existing language/framework instructions
+
+if you are curious about the bigger project, it is under:
+
+* GitHub: `onestardao / WFGY`
+* the WFGY 2.0 core is the engine behind that work
+
+but you do not need to go there to try this.
+
+this instructions file is meant as a small gift for copilot users:
+a way to experiment with a mathematical “reasoning core”
+without leaving your editor.

plugins/awesome-copilot/.github/plugin/plugin.json

@@ -15,12 +15,12 @@
     "agents"
   ],
   "agents": [
-    "./agents/meta-agentic-project-scaffold.md"
+    "./agents"
   ],
   "skills": [
-    "./skills/suggest-awesome-github-copilot-skills/",
-    "./skills/suggest-awesome-github-copilot-instructions/",
-    "./skills/suggest-awesome-github-copilot-prompts/",
-    "./skills/suggest-awesome-github-copilot-agents/"
+    "./skills/suggest-awesome-github-copilot-skills",
+    "./skills/suggest-awesome-github-copilot-instructions",
+    "./skills/suggest-awesome-github-copilot-prompts",
+    "./skills/suggest-awesome-github-copilot-agents"
   ]
 }

plugins/awesome-copilot/agents/meta-agentic-project-scaffold.md

@@ -0,0 +1,16 @@
+---
+description: "Meta agentic project creation assistant to help users create and manage project workflows effectively."
+name: "Meta Agentic Project Scaffold"
+tools: ["changes", "codebase", "edit/editFiles", "extensions", "fetch", "findTestFiles", "githubRepo", "new", "openSimpleBrowser", "problems", "readCellOutput", "runCommands", "runNotebooks", "runTasks", "runTests", "search", "searchResults", "terminalLastCommand", "terminalSelection", "testFailure", "updateUserPreferences", "usages", "vscodeAPI", "activePullRequest", "copilotCodingAgent"]
+model: "GPT-4.1"
+---
+
+Your sole task is to find and pull relevant prompts, instructions and chatmodes from https://github.com/github/awesome-copilot
+All relevant instructions, prompts and chatmodes that might be able to assist in an app development, provide a list of them with their vscode-insiders install links and explainer what each does and how to use it in our app, build me effective workflows
+
+For each please pull it and place it in the right folder in the project
+Do not do anything else, just pull the files
+At the end of the project, provide a summary of what you have done and how it can be used in the app development process
+Make sure to include the following in your summary: list of workflows which are possible by these prompts, instructions and chatmodes, how they can be used in the app development process, and any additional insights or recommendations for effective project management.
+
+Do not change or summarize any of the tools, copy and place them as is

plugins/awesome-copilot/skills/suggest-awesome-github-copilot-agents/SKILL.md

@@ -0,0 +1,106 @@
+---
+name: suggest-awesome-github-copilot-agents
+description: 'Suggest relevant GitHub Copilot Custom Agents files from the awesome-copilot repository based on current repository context and chat history, avoiding duplicates with existing custom agents in this repository, and identifying outdated agents that need updates.'
+---
+
+# Suggest Awesome GitHub Copilot Custom Agents
+
+Analyze current repository context and suggest relevant Custom Agents files from the [GitHub awesome-copilot repository](https://github.com/github/awesome-copilot/blob/main/docs/README.agents.md) that are not already available in this repository. Custom Agent files are located in the [agents](https://github.com/github/awesome-copilot/tree/main/agents) folder of the awesome-copilot repository.
+
+## Process
+
+1. **Fetch Available Custom Agents**: Extract Custom Agents list and descriptions from [awesome-copilot README.agents.md](https://github.com/github/awesome-copilot/blob/main/docs/README.agents.md). Must use `fetch` tool.
+2. **Scan Local Custom Agents**: Discover existing custom agent files in `.github/agents/` folder
+3. **Extract Descriptions**: Read front matter from local custom agent files to get descriptions
+4. **Fetch Remote Versions**: For each local agent, fetch the corresponding version from awesome-copilot repository using raw GitHub URLs (e.g., `https://raw.githubusercontent.com/github/awesome-copilot/main/agents/<filename>`)
+5. **Compare Versions**: Compare local agent content with remote versions to identify:
+   - Agents that are up-to-date (exact match)
+   - Agents that are outdated (content differs)
+   - Key differences in outdated agents (tools, description, content)
+6. **Analyze Context**: Review chat history, repository files, and current project needs
+7. **Match Relevance**: Compare available custom agents against identified patterns and requirements
+8. **Present Options**: Display relevant custom agents with descriptions, rationale, and availability status including outdated agents
+9. **Validate**: Ensure suggested agents would add value not already covered by existing agents
+10. **Output**: Provide structured table with suggestions, descriptions, and links to both awesome-copilot custom agents and similar local custom agents
+    **AWAIT** user request to proceed with installation or updates of specific custom agents. DO NOT INSTALL OR UPDATE UNLESS DIRECTED TO DO SO.
+11. **Download/Update Assets**: For requested agents, automatically:
+    - Download new agents to `.github/agents/` folder
+    - Update outdated agents by replacing with latest version from awesome-copilot
+    - Do NOT adjust content of the files
+    - Use `#fetch` tool to download assets, but may use `curl` using `#runInTerminal` tool to ensure all content is retrieved
+    - Use `#todos` tool to track progress
+
+## Context Analysis Criteria
+
+🔍 **Repository Patterns**:
+
+- Programming languages used (.cs, .js, .py, etc.)
+- Framework indicators (ASP.NET, React, Azure, etc.)
+- Project types (web apps, APIs, libraries, tools)
+- Documentation needs (README, specs, ADRs)
+
+🗨️ **Chat History Context**:
+
+- Recent discussions and pain points
+- Feature requests or implementation needs
+- Code review patterns
+- Development workflow requirements
+
+## Output Format
+
+Display analysis results in structured table comparing awesome-copilot custom agents with existing repository custom agents:
+
+| Awesome-Copilot Custom Agent                                                                                                                            | Description                                                                                                                                                                | Already Installed | Similar Local Custom Agent         | Suggestion Rationale                                          |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------- | ---------------------------------- | ------------------------------------------------------------- |
+| [amplitude-experiment-implementation.agent.md](https://github.com/github/awesome-copilot/blob/main/agents/amplitude-experiment-implementation.agent.md) | This custom agent uses Amplitude's MCP tools to deploy new experiments inside of Amplitude, enabling seamless variant testing capabilities and rollout of product features | ❌ No             | None                               | Would enhance experimentation capabilities within the product |
+| [launchdarkly-flag-cleanup.agent.md](https://github.com/github/awesome-copilot/blob/main/agents/launchdarkly-flag-cleanup.agent.md)                     | Feature flag cleanup agent for LaunchDarkly                                                                                                                                | ✅ Yes            | launchdarkly-flag-cleanup.agent.md | Already covered by existing LaunchDarkly custom agents        |
+| [principal-software-engineer.agent.md](https://github.com/github/awesome-copilot/blob/main/agents/principal-software-engineer.agent.md)                 | Provide principal-level software engineering guidance with focus on engineering excellence, technical leadership, and pragmatic implementation.                            | ⚠️ Outdated       | principal-software-engineer.agent.md | Tools configuration differs: remote uses `'web/fetch'` vs local `'fetch'` - Update recommended |
+
+## Local Agent Discovery Process
+
+1. List all `*.agent.md` files in `.github/agents/` directory
+2. For each discovered file, read front matter to extract `description`
+3. Build comprehensive inventory of existing agents
+4. Use this inventory to avoid suggesting duplicates
+
+## Version Comparison Process
+
+1. For each local agent file, construct the raw GitHub URL to fetch the remote version:
+   - Pattern: `https://raw.githubusercontent.com/github/awesome-copilot/main/agents/<filename>`
+2. Fetch the remote version using the `fetch` tool
+3. Compare entire file content (including front matter, tools array, and body)
+4. Identify specific differences:
+   - **Front matter changes** (description, tools)
+   - **Tools array modifications** (added, removed, or renamed tools)
+   - **Content updates** (instructions, examples, guidelines)
+5. Document key differences for outdated agents
+6. Calculate similarity to determine if update is needed
+
+## Requirements
+
+- Use `githubRepo` tool to get content from awesome-copilot repository agents folder
+- Scan local file system for existing agents in `.github/agents/` directory
+- Read YAML front matter from local agent files to extract descriptions
+- Compare local agents with remote versions to detect outdated agents
+- Compare against existing agents in this repository to avoid duplicates
+- Focus on gaps in current agent library coverage
+- Validate that suggested agents align with repository's purpose and standards
+- Provide clear rationale for each suggestion
+- Include links to both awesome-copilot agents and similar local agents
+- Clearly identify outdated agents with specific differences noted
+- Don't provide any additional information or context beyond the table and the analysis
+
+## Icons Reference
+
+- ✅ Already installed and up-to-date
+- ⚠️ Installed but outdated (update available)
+- ❌ Not installed in repo
+
+## Update Handling
+
+When outdated agents are identified:
+1. Include them in the output table with ⚠️ status
+2. Document specific differences in the "Suggestion Rationale" column
+3. Provide recommendation to update with key changes noted
+4. When user requests update, replace entire local file with remote version
+5. Preserve file location in `.github/agents/` directory