Install
$ agentstack add skill-adit-jain-srm-skill-forge-context-builder ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Context Builder
Overview
Create a shared vocabulary between you and the agent. Once built, the agent speaks YOUR language — concise, precise, domain-native.
The Problem This Solves
Without shared context:
- Agent uses 20 words where your domain has 1 term
- Variables named generically ("data", "handler", "service") instead of domain terms
- Every session starts from scratch explaining the same concepts
- Conversations are verbose because the agent doesn't know your jargon
With CONTEXT.md:
- Agent speaks your domain language natively
- "the materialization cascade" replaces "when a lesson inside a section of a course is made real"
- Code uses domain terms for variables, functions, modules
- Sessions start with shared understanding already loaded
Process
1. Interview (grill the user)
Ask these questions ONE AT A TIME:
- "What is this project? One sentence."
- "What are the 3-5 most important CONCEPTS in this domain?"
- For each concept: "Define it in one sentence. What terms should I AVOID using for this?"
- "Are there relationships between these concepts? (X contains many Y, Y belongs to one X)"
- "Any terms that were previously confusing or ambiguous? What did you resolve them to?"
- "What actions/verbs are specific to this domain?" (e.g., "materialize", "triage", "hydrate")
2. Generate CONTEXT.md
Write to the project root:
# [Project Name]
[One-sentence description]
## Language
**[Term 1]**:
[Definition in one sentence]
_Avoid_: [terms NOT to use for this concept]
**[Term 2]**:
[Definition]
_Avoid_: [alternatives to avoid]
## Relationships
- A **[Term 1]** contains many **[Term 2]**s
- A **[Term 2]** belongs to one **[Term 1]**
## Flagged Ambiguities
- "[confusing term]" was previously used for both X and Y — resolved: [how it's now used]
3. Persistence
Once CONTEXT.md exists:
- Agent reads it at session start
- Uses defined terms in ALL communication
- Names variables/functions using the vocabulary
- Flags when new undefined terms appear: "Should I add '[term]' to CONTEXT.md?"
4. Evolution
CONTEXT.md grows over time:
- New concepts emerge during development → add them
- Ambiguities surface → resolve and document
- Terms become obsolete → remove them
- The vocabulary becomes MORE precise with each session
Verification
After generating CONTEXT.md:
- [ ] Every domain concept has exactly ONE term (no synonyms in use)
Concrete Example
For an e-commerce project, a good CONTEXT.md entry:
**Cart**:
A temporary collection of products a customer intends to purchase. Exists per-session, persists across page navigation, cleared on checkout completion.
_Avoid_: basket, bag, order (an Order only exists AFTER checkout)
**SKU**:
The unique identifier for a specific product variant (size + color). NOT the product itself — one Product has many SKUs.
_Avoid_: product ID, item number (SKU is variant-level, product ID is parent-level)
This level of precision prevents: wrong variable names (orderId when it should be cartId), confused queries (joining on productid when you need skuid), and verbose explanations ("the thing where users put stuff before buying").
- [ ] Each term has an "Avoid" list (prevents drift back to vague language)
- [ ] Relationships are explicit (not just a flat list)
- [ ] The agent can explain the project using ONLY these terms
Common Mistakes
- Adding implementation details to CONTEXT.md (it's a GLOSSARY, not a spec)
- Defining too many terms at once (start with 5-8 core terms, grow organically)
- Skipping the "Avoid" list (without it, the agent drifts back to generic language)
- Treating it as write-once (update when new domain concepts emerge)
Why This Works
Cognitive science: shared mental models reduce communication overhead exponentially. In software, Eric Evans called this "Ubiquitous Language" (Domain-Driven Design). mattpocock proved it works for AI agents with 75% token reduction in practice.
The CONTEXT.md IS the Ubiquitous Language for your human-agent collaboration.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Adit-Jain-srm
- Source: Adit-Jain-srm/skill-forge
- License: MIT
- Homepage: https://github.com/Adit-Jain-srm/skill-forge#quick-start
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.