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 skills/t-rav/hydraflow/hf.memorynpx skills add T-rav/hydraflow --skill hf.memorygit clone --depth 1 https://github.com/T-rav/hydraflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/t-rav/hydraflow/hf.memory)<a href="https://agentmods.dev/skills/t-rav/hydraflow/hf.memory"><img src="https://agentmods.dev/badge/skills/t-rav/hydraflow/hf.memory.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00010 | $0.00913 |
| Opus 5 | $0.00005 | $0.00456 |
| Sonnet 5 | $0.00002 | $0.00183 |
| Haiku 4.5 | $0.00001 | $0.00091 |
Grade A, and why
hf.memory 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 3d ago.
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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture Session Learnings as Memory Suggestions
Scan the current conversation for architectural decisions, bug root causes, configuration choices, codebase patterns, and workflow preferences. Write each as a memory item to the local JSONL store for ingestion by the memory sync worker.
Usage
/hf.memory
/hf.memory review model decisions
$ARGUMENTS optionally filters extraction to a specific topic. If empty, scan the full conversation.
Instructions
Phase 0: Resolve Configuration
Before doing anything else, resolve these values:
- REPO: Run
echo "$HYDRAFLOW_GITHUB_REPO". If empty, rungit remote get-url originand extract theowner/reposlug (striphttps://github.com/prefix and.gitsuffix). - DATA_ROOT: The HydraFlow data directory. Check
echo "$HYDRAFLOW_DATA_ROOT". If empty, default to.hydraflow/<repo_slug>/relative to repo root (where/in the slug is replaced with-).
Phase 1: Fetch Existing Memories for Dedup
Load existing memory items from the local JSONL store to avoid duplicates:
cat "$DATA_ROOT/memory/items.jsonl" 2>/dev/null | jq -r '.title' | sort -u
Keep these titles in mind — skip any learning that substantially overlaps with an existing item.
Phase 2: Extract Learnings from Conversation
Scan the full conversation history (or filter to $ARGUMENTS topic if provided). Look for these categories:
- Architectural decisions — choices about how the system is structured
- Bug root causes and fixes — what broke, why, and how it was fixed
- Configuration insights — what settings mean and why they're set that way
- Codebase patterns — recurring patterns that future agents should follow
- Workflow preferences — how the developer wants things done
For each learning, formulate:
- title: Short description (under 60 chars)
- learning: What was learned and why it matters (1-3 sentences)
- context: How it was discovered — reference specific issues, PRs, files, or conversation topics
- memory_type: One of
knowledge,config,instruction,code
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.
- 3d ago First seen · 94 lines · 10 tokens per session scan A 10af7412e3d4
hf.memory is a skill published in the GitHub repository T-rav/hydraflow (5 stars, last pushed 3d ago), licensed Apache-2.0. It adds 10 tokens to every session and 913 once invoked, about $0.0001 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.
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develop
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sync
Run /sync as the last step after a change is complete, around merge, to keep durable knowledge current. Updates root and nested AGENTS.md, reconciles the scope from repo evidence, and flags specs the change made stale. Surgical edits only: it adds lines, and rewrites single lines it owns. Never a whole section, never…
test
Run /test to write a test suite for code you just built or changed, after implementing a feature, route, or fix. Targets uncommitted changes automatically, reads test preferences.json for your framework (asks and saves it if absent), and picks the right strategy per file: happy path, edge cases, error states…
document
Run /document pr | changelog | release-note | postmortem (or let it ask) to write the human facing prose about a change. Drafts from the real commits and diff, writing to the right place. Does not write code, tests, or specs.
debug
Run /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.
check
Confirm a change before merge. /check verify drives the real app to prove behavior against the spec (every acceptance criterion met, every surface built). /check review runs a senior code review on a fresh model, one that did not write the code. Verify after /develop, review before a PR. Writes to docs/reviews/, never…