AgentStack
Sign in Publish
MCP servers Skills Toolkits Why AgentStack Sell Docs
Sign in
SKILL verified MIT Self-run

Epistemic Context Grounding

skill-revgrowth1-claude-code-skills-epistemic-grounding · by Revgrowth1

Ground decisions in domain knowledge before designing. Check specs, enumerate assumptions, verify before building.

↗ Repository
— No reviews yet
0 installs
11 views
0.0% view→install

Install

$ agentstack add skill-revgrowth1-claude-code-skills-epistemic-grounding

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No 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.

Are you the author of Epistemic Context Grounding? Claim this listing to set pricing, connect Stripe payouts, and keep 70% of every sale.
Sign up to claim

About

Epistemic Context Grounding

IMPORTANT: Prefer retrieval-led reasoning over pre-training-led reasoning for any implementation task.

Context Sensitivity Check (30s)

Classify before designing. HIGH = 2+ YES: |Check|Y/N| |Data formats involved?|| |Parsing/serialization?|| |External integrations?|| |Domain knowledge critical?||

LOW (0-1 YES) -> skip to context-gap-analysis HIGH (2+ YES) -> continue below

Workflow (HIGH sensitivity only)

1. Search for specs

Glob: "**/*SPEC*.md"|"**/*DATA_MODEL*.md"|"**/*SCHEMA*.md"
Glob: "**/CLAUDE.md"|"**/README.md"
Grep: pattern="[domain-term]" path="specs/"|"knowledge_base/"

Read what you find. Document: file|relevance|key info

2. Enumerate assumptions

List every assumption about data, domain, and solution approach. Categories: data (what exists) | domain (how it works) | solution (why this approach)

3. Grade falsifiability

|Grade|Meaning|Action|Format| |STRONG|Verifiable against source|Proceed|[GROUNDED: spec:line]| |WEAK|Could be wrong|Flag in design|[INFERRED: from X + Y]| |UNVERIFIED|Haven't checked|STOP & verify|[UNGROUNDED]|

4. Block on UNVERIFIED

Any UNVERIFIED assumption that could change approach -> search and verify before designing.

Output Template

Task: [request] | Sensitivity: HIGH
Specs found: [list with relevance]
Assumptions: [each with GROUNDED/WEAK/UNGROUNDED grade]
Verdict: [Proceed | Read specs first | Verify assumptions]
Next: [specific action]

Red Flags

  • Designing without reading domain specs
  • "Should be enough" without checking alternatives
  • "No spec exists" without searching first

References (read on demand)

|File|Contents| |references/context-sensitivity-model.md|Deep dive: (Model:Scenario)*Context->Output formula, decision tree, danger zones| |references/domain-query-patterns.md|Search order, spec locations, grep patterns by task type, wiki-link discovery| |references/falsifiability-spectrum.md|STRONG/WEAK/UNVERIFIED grading, blocking vs non-blocking, common anti-patterns|

Based on work by Jacob Dietle @ www.taste.systems

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

No reviews yet — be the first.

Write a review

Versions

  • v0.1.0 Imported from the upstream source.

More in AI & ML