Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/davesheffer/hunch/hunchgit clone --depth 1 https://github.com/davesheffer/hunchWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01529 | $0.01529 |
| Opus 5 | $0.00764 | $0.00764 |
| Sonnet 5 | $0.00306 | $0.00306 |
| Haiku 4.5 | $0.00153 | $0.00153 |
Grade A, and why
hunch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🧠 Hunch (Engineering Memory)
This repo has Hunch — a curated graph of why the code is the way it is (decisions, bug history, invariants). It currently holds 193 decisions, 2 bugs, 10 constraints, 19 components, 3 policies, 1 open findings.
Consult Hunch via the hunch_* MCP tools — pick by MOMENT, not from memory:
Orient (session/task start):
hunch_context(target)— the minimal relevant slice for what you're about to do; a task phrase falls back to the closest graph matches. Call FIRST.hunch_structure(target?)— the indexed shape of the repo/dir/file/symbol — orient from the graph, not grep rounds.hunch_runbook(task)— the proven steps for a recurring task, before re-deriving them.hunch_escalations()— the decisions only the HUMAN can make (including one exact imported ADR at a time, topic conflicts, and policy calls). Normally empty; when it isn't, ASK the user inline — an entry is a question, silence is never approval. Apply an ADR answer only throughhunch_review_imported_adrwith its printed source and review hashes.hunch now(CLI) — recent decisions + the live roadmap;hunch log— the memory-move timeline (every capture/adopt/supersede/prune/repair, each revertable).
Before designing / choosing an approach:
hunch_why(target)— why a file/symbol is shaped this way (decisions, bugs, constraints) — including what was already REJECTED.hunch_current_decision(topic)— the one live answer for a topic (history + rejected included).hunch_bug_lineage(symptom_or_symbol)— has this failed before? what was the root cause?hunch_compare(candidates)— rank candidate branches/commits by fewest invariant hits.hunch_query(query)— free-text search when nothing above fits.
Before editing:
hunch_check_constraints(scope)andhunch_get_dependents(symbol)/hunch_blast_radius(target)— invariants in scope + who you'd break. (The pre-edit hook injects this per file automatically; call these for PLANNING breadth.)hunch_findings(scope?)— known-but-unfixed gaps in the area (past audits, measurements, incidents) so you inherit them instead of re-discovering them.
Before committing / merging:
hunch_conformance()— does the code still SATISFY recorded intent? Run before and after a refactor.hunch_policy_evaluate(policy_id?, active_only?)/hunch_policy_plan(policy_id)/hunch_policy_card(policy_id)/hunch_policy_proof(policy_id)— evaluate canonical policy, inspect the planned corpus, review the evidence/uncertainty card, and inspect raw replay receipts; only an explicit human activation grants authority.hunch_pr_impact(base?)/hunch_merge_verdict(...)— a change's memory surface; would it re-open a closed bug?
Build the Constitution review queue:
hunch constitution bootstrap --since 90d --max-candidates 3(CLI) — normalize recent structured human evidence into at most three non-active policy candidates; add--historyfor exact, human-identifier-grounded fix/revert deltas or explicit dependency retirements. Coincidence/ambiguity stays uncompilable; neither path grants authority.hunch constitution ingest --since 90d [--instructions] [--from export.json](CLI) — normalize corrections/failures plus bounded committed instructions/ADRs and strict local review/conversation/PR exports into Git-native evidence; raw prose is hash-only, unsupported intent remains uncompilable, and no policy is minted.
After deciding / when corrected:
hunch_capture_decision(topic?)→hunch_record_decision(...)— interview first, then write; statusproposed= roadmap intent (shows inhunch now).hunch_record_correction(...)— a human correction becomes an ENFORCED rule (Never Twice), not a one-session memory.hunch_record_finding(...)— an OBSERVATION with no code change (an audit that found a gap, a measured number, an incident) becomes durable memory anchored to a date + evidence;/auditruns the ritual.hunch_timeline(target)— decision history when investigating how something evolved.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 58 lines · 1,529 tokens per session scan A c53f1fbfdb64
hunch is a cursor rule published in the GitHub repository davesheffer/hunch (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 1,529 tokens to every session, about $0.0076 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
050-plan
When the user types /plan or asks to create a project plan, feature PRD, or retrospective.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
graphql
GraphQL: schema design, resolvers, performance.
context-routing
System, platform, and developer instructions take precedence; then repository-specific rules; then this framework. Before work, read .ai/bootstrap/boot.md and .ai/rules/00-master-rules.md. Load focused rules and skills only when triggered.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.