Signals
PostHog/posthog
How to query the documentembeddings table for raw signal data using HogQL.
Write a high-signal mathematical synthesis from global memory and the fact graph for the Codex main agent's own strategy and worker dispatch.
$ npx skills add frenzymath/Danus --skill elaboration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install frenzymath/Danus elaboration --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/elaboration .claude/skills/elaboration && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .claude/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaborationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add frenzymath/Danus --skill elaboration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install frenzymath/Danus elaboration --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/elaboration .agents/skills/elaboration && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .agents/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add frenzymath/Danus --skill elaboration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install frenzymath/Danus elaboration --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/elaboration .cursor/skills/elaboration && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .cursor/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/frenzymath/Danus.git --path .agents/skills/elaboration--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add frenzymath/Danus --skill elaboration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install frenzymath/Danus elaboration --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/elaboration .gemini/skills/elaboration && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .gemini/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install frenzymath/Danus elaborationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add frenzymath/Danus --skill elaboration -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/elaboration .github/skills/elaboration && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .github/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add frenzymath/Danus --skill elaboration -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install frenzymath/Danus elaboration --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/elaboration .opencode/skills/elaboration && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "elaboration" agent skill from https://github.com/frenzymath/Danus/tree/codex/.agents/skills/elaboration into .opencode/skills/elaboration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "elaboration", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
elaborationWrite a high-signal mathematical synthesis from global memory and the fact graph for the Codex main agent's own strategy and worker dispatch.
Elaboration is an agent skill from frenzymath/Danus. Write a high-signal mathematical synthesis from global memory and the fact graph for the Codex main agent's own strategy and worker dispatch.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory. The licence is Apache-2.0.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6d92e8d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Elaboration loads about 3.7k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 1,933 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from frenzymath/Danus at commit 6d92e8d, republished under its Apache-2.0 licence (© frenzymath). 1,933 words, ~3,651 tokens.
.claude/skills/elaboration/SKILL.md (or your agent's skills folder).You are the main agent. At each strategic cycle, including the global review
on each roughly 30-minute heartbeat, you distill the project's current state into
one elaboration when the synthesis materially changes: a readable, deeply
analytical synthesis for your own reasoning and worker dispatch. The heartbeat
itself always requires a fresh global appraisal, but it need not create a
duplicate elaboration when nothing material changed. You may ask speculative
Codex subagents to explore individual gaps or undertake sustained technical
reasoning, but their reports remain unverified and must be labelled as
hypotheses. You author the next master_guidance yourself.
The elaboration is also what you draw on to keep the operator informed.
A well-formed elaboration satisfies all of the following — a worker or linter can check them mechanically:
_(no signed-closed components yet)__(no failed or obsolete routes recorded; absence here does not mean the strategy is unique)_fact_id cited exists in the fact graph; no invented ids, no
paraphrase substituted for a verified statement.gm_add(kind="elaboration", …) with verifiable left at its
default (false).Read only the shared stores — never a worker's private local memory (a layer
boundary, and the reason this is cleaner than a log-scraping summary agent). All
reads are project-scoped for the main agent (project=<p>):
verification traces, the
current master_guidance. Read via gm_search, or as a fallback by reading the
raw runtime/projects/<p>/global_memory/<kind>.jsonl files.fact_search, or as a fallback by reading the raw
runtime/projects/<p>/fact_graph/facts/*.md files: what is established vs. still
open, and how facts compose.Quote the goal and do not change or weaken it — do not redefine, simplify, restrict to a special case, or substitute an easier proxy. If the evidence suggests the goal may be false or unreachable by the current strategy, say so plainly while keeping the goal fixed.
Produce one markdown document with these sections, in order. Omit a section's body only by writing the honest empty-state line, never by dropping the heading.
Open with one of these, in bold on its own line:
Not solved. … | Counterexample found. … | Verified complete proof. … | Solved. …
Then:
fact_ids).fact_ids).Then a status dashboard (one table) with at least these rows: Fixed goal (UNCHANGED, with goal text); Verified complete proof (YES/NO); Verified counterexample (YES/NO); Signed-closed sub-tasks (count + names); Main blocker (a specific lemma, not vague); Routes marked false/obsolete (YES/NO + which); Highest-priority unresolved task (P0/P1/P2 with the exact mathematical task).
Then a sub-task status summary (one table: Sub-task | Status | Closed facts | Conditional facts | Main missing interface), one row per sub-task the problem enumerates. Use only these UPPERCASE labels:
Strict CLOSED test. For each sub-task you are tempted to mark CLOSED, ask: "Is there a verified fact that handles this on the actual construction, with zero remaining hypothesis to match?" If you cannot answer YES with a specific
fact_idand zero remaining work, mark SUBSTANTIAL. Over-marking CLOSED is the single most damaging error here — it reads as "no further work needed."
Then an approach portfolio (one table: Approach | Mechanism | Mathematical
frontier | Decisive obstacle | Evidence for/against | Active/parked | Revisit
condition). Include every credible route still worth remembering, not only the
currently dominant route. Preserve parked routes and their return conditions so
that recent work cannot silently erase a serious alternative. If a major route
choice has changed, state the alternatives considered and the mathematical
reason for the change; this decision must also be preserved in the subsequent
master_guidance.
End §0 with Current best proof skeleton (6–12 short numbered lines: the
smallest structure that closes the goal if the central missing lemma were
known, with fact_ids where facts apply) and Central missing lemma (the
single most precise unresolved statement, at full precision — all quantifiers,
definitions inlined for self-containment, and one short "why this is non-trivial"
paragraph if warranted).
<math content> — fact_ids
…") or the line _(no signed-closed components yet)_.fact_id / concrete obstruction), or
_(no failed or obsolete routes recorded; absence here does not mean the strategy is unique)_.
FALSE AS STATED = a plausible reduction now refuted; OBSOLETE = superseded by a
simpler live route.The single most important diagnostic — a human reader uses it to find exactly which input/output hypothesis is unmatched on the actual model. For each interface in the proof architecture (use the exact sub-task names the problem enumerates: per-stage A/B/C…, each transition B→C, C→D…, and the meta-reduction to the original statement):
<Interface name> — <one-line role in the proof>
Input required. <precise mathematical conditions step i+1 demands of step i's output — normality, Q-factoriality, R-Cartierness, dimension, …; not just "compatibility"> Output claimed. <what the existing conditional package guarantees, conditional on its own hypotheses> Available facts.
fact_id— one-line statement; … Missing verification on the actual model. <numbered: the specific hypothesis-matches not yet carried out on the actual construction> Failure mode if ignored. <one or two sentences: what concretely breaks downstream — e.g. "If K_W+B_W is not Q-Cartier, 'by negativity lemma' is vacuous and the crepancy conclusion is unjustified."> Status. <one of the seven labels>
Do not skip an interface even if its row is trivial — flag trivial matches so a cold reader knows they were considered. Inline the definitions of load-bearing terms so a cold reader need not consult the problem statement. If the problem is built around a single central lemma rather than a pipeline, produce one interface row for the central reduction in the same format. Strict CLOSED rule applies per row: if "Missing verification" is non-empty, the status is SUBSTANTIAL or weaker — never CLOSED.
fact_ids; give the correct rephrasing where one is needed.Identify 2–4 bridge lemmas — concrete intermediate facts that, if proven, would either prove the central missing lemma or unblock a specific interface row. For each:
Bridge k — <short name>
Target statement. <full, precise mathematical statement> Prerequisites. <conditions the inputs must satisfy for the statement to type-check> Existing facts to use.
fact_id— one-line claim; … (only ids that actually exist) Missing checks. <numbered: the specific hypothesis-matches / sub-proofs still needed> Closure criterion. <one paragraph naming the exact proof obligation that closes this bridge>
Order by leverage: Bridge 1 unblocks the most downstream / has the highest payoff per unit effort. State for each whether it is independent (parallelizable) or dependent — this is what lets you put different workers on different bridges.
fact_id
is awareness, not truth.master_guidance and worker assignments.search_arxiv_theorems broadly with varied formulations and
technique names. Record a concise technique map in global memory: mechanisms,
exact assumptions, limitations, relevant arXiv identifiers/results, and
possible interfaces with this problem. Understand and adapt established
strategies before inventing new machinery; literature notes are not facts.Publish the elaboration to global memory with gm_add:
kind: elaborationclaim: the §0 verdict line (the bolded opener + the one-line main blocker)evidence: the full five-section markdown bodylinks: {"fact_ids": ["…", "…"]} — the facts you cited (only ids that exist
in the fact graph)verifiable defaults to false for this kind — it is a synthesis/judgment,
not an objectively checkable claim; leave it unset.)Then reason over the elaboration yourself. Optionally give precise pieces to
exploratory subagents, label their reports unverified, synthesize the result into
master_guidance, and dispatch Danus workers afterward.
Reference the role=main MCP tools by name (never internal engine paths):
gm_search / read runtime/projects/<p>/global_memory/<kind>.jsonl — gather
findings, dead ends, recent verifications, current master_guidance.fact_search / read runtime/projects/<p>/fact_graph/facts/*.md — the verified
facts and the DAG (fact_search to pull the facts bearing on a sub-task; read
the files for the full statements/proofs and predecessor structure).gm_add (kind elaboration) — publish the synthesis.search_arxiv_theorems — use repeatedly with varied formulations and technique
names to map the relevant literature, understand established mechanisms and
hypotheses, and check whether missing bridges or nearby results already exist.© frenzymath, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/elaboration of frenzymath/Danus.
Open the folder on GitHubat commit 6d92e8d
Elaboration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Elaboration this skillfrenzymath/Danus | 476 | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| SignalsPostHog/posthog | 40k | — | ~4.3k | Automated safety check: Pass | Custom licence | |
| Trader Signalruvnet/ruflo | 74k | — | ~605 | Automated safety check: Notes | MIT | |
| Agent Signal Pipelinelobehub/lobehub | 83k | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| Signal Writegithub/awesome-copilot | 40k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Signal Channel for NanoClawnanocoai/nanoclaw | 31k | — | ~3.6k | Automated safety check: Notes | MIT |
PostHog/posthog
How to query the documentembeddings table for raw signal data using HogQL.
ruvnet/ruflo
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
lobehub/lobehub
Guides building event-driven background work for LobeHub agents, with sources, signals, actions, policies, workflow handoff and deduplication.
github/awesome-copilot
Emit structured agent signals — hands-up, blocked, done, checkpoint, partnership.
nanocoai/nanoclaw
Links NanoClaw to Signal as a secondary device on your existing phone, with a native adapter that talks to a signal-cli daemon and needs no bot API.
sickn33/agentic-awesome-skills
Fetch and explain TraderSpy's AI crypto futures signals: entry, take-profit ladder, stop, triggers, status against the live price, and how recent signals resolved.
frenzymath/Danus
Validate externally referenced theorems by querying arXiv theorem search first and Codex's built-in web search second.
frenzymath/Danus
Construct candidate counterexamples to test a proposed conjecture, lemma, or intermediate claim by keeping the assumptions true while making the claimed conclusion fail.
frenzymath/Danus
Generate and analyze simpler examples that satisfy both the assumptions and the conclusion of a theorem statement or subgoal.
frenzymath/Danus
Screen a decomposition plan by first trying to prove all of its subgoals directly, then identifying the key stuck points if the plan does not fully go through.
frenzymath/Danus
Synthesize the common stuck points across failed decomposition plans.
frenzymath/Danus
Derive immediate mathematical consequences from a theorem statement or subgoal.
Write a high-signal mathematical synthesis from global memory and the fact graph for the Codex main agent's own strategy and worker dispatch. Elaboration is an agent skill from frenzymath/Danus. Write a high-signal mathematical synthesis from global memory and the fact graph for the Codex main agent's own strategy and worker dispatch.
Run `npx skills add frenzymath/Danus --skill elaboration -a claude-code`. Or copy the skill folder (.agents/skills/elaboration in frenzymath/Danus) into .claude/skills/elaboration in your project. Claude Code loads it when a task matches its description.
Run `npx skills add frenzymath/Danus --skill elaboration -a codex`. Or copy the skill folder (.agents/skills/elaboration in frenzymath/Danus) into .agents/skills/elaboration in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add frenzymath/Danus --skill elaboration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/elaboration, .gemini/skills/elaboration, .github/skills/elaboration and .opencode/skills/elaboration in your project.
SKILL.md names no scripts, command-line tools or credentials: Elaboration is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Elaboration is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Elaboration: Signals (PostHog/posthog, 40k stars), Trader Signal (ruvnet/ruflo, 74k stars), Agent Signal Pipeline (lobehub/lobehub, 83k stars) and Signal Write (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
frenzymath (a GitHub organization) maintains it in frenzymath/Danus, which has 476 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on August 27, 2026.
Source: frenzymath/Danus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.