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/frenzymath/danus/direct-provingnpx skills add frenzymath/Danus --skill direct-provinggit clone --depth 1 https://github.com/frenzymath/DanusWrote 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/frenzymath/danus/direct-proving)<a href="https://agentmods.dev/skills/frenzymath/danus/direct-proving"><img src="https://agentmods.dev/badge/skills/frenzymath/danus/direct-proving.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 | $0.00044 | $0.01134 |
| Opus 5 | $0.00022 | $0.00567 |
| Sonnet 5 | $0.00009 | $0.00227 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
direct-proving 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 5d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Direct Proving
Use this skill to screen decomposition plans by first trying to carry the whole plan through, and if it does not fully go through, then identify the key stuck points.
Input Contract
Read:
- one decomposition plan from
subgoals - relevant
immediate_conclusions,toy_examples,counterexamples, andfailed_paths - relevant search results and references
- any previously identified external statements whose proofs may be adaptable
Procedure
- Take one decomposition plan at a time.
- For each subgoal, actively use the searched results, toy examples, and counterexamples that are most relevant to that subgoal.
- When a similar theorem has been found, try to adapt its proof idea, construction, or reduction to the current subgoal instead of treating it as a black-box citation.
- If the adapted theorem is only a partial result with extra hypotheses, first analyze why its method needs those hypotheses and where it fails for the current subgoal — do not skip this by merely trying to show the current object satisfies the extra hypotheses and applying the partial result directly.
- First attempt to prove all subgoals in that plan directly.
- Try to carry the whole plan through before switching into failure diagnosis mode.
- For each subgoal, record whether it is:
- already solved directly
- partially advanced
- blocked
- If a subgoal is blocked or you get stuck on it, FIRST invoke
$construct-counterexamplesfor that subgoal — test whether it is false, too strong, or missing hypotheses (not merely hard). If no counterexample emerges and the subgoal still resists after at least two genuine direct attempts, do not grind indefinitely: record the stuck point as anobstacle/dead_endfinding (gm_add) so siblings skip it and the next round'smaster_guidancecan bring fresh direction. - If a proof adaptation attempt fails, identify why the migration fails. Be concrete: for example, note which hypothesis is missing, which construction does not transfer, which step breaks, which counterexample blocks the migration, or which part of the searched proof depends on structure absent in the current setting.
- If a subgoal is solved with a self-contained partial result that the rest of the plan will USE downstream, partial-verify that result with
$verify-proofin partial-candidate mode before treating it as established. Adopting unverified partial results as building blocks is the single biggest correctness risk; the verifier is the sole authority on whether the partial result really holds. - If all subgoals are solved directly AND the partial results that compose into a full proof have each been partial-verified as needed, mark the plan as solved and assemble the proof draft.
- When a direction remains viable and supports sustained progress, work on it deeply for 1–2 hours and try to establish one mathematically deep result that resolves a genuine obstacle or materially advances the main problem. Do not turn each routine calculation into its own fact, and do not bundle shallow observations merely to imitate depth; use supporting steps to prove the one substantive conclusion.
- If the plan does not fully go through, then identify the key stuck points as concretely as possible.
- Focus on locating the decisive failure modes of the plan after this first full attempt, not on polishing a full proof.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 86 lines · 44 tokens per session scan A 9e1c22241444
direct-proving is a skill published in the GitHub repository frenzymath/Danus (401 stars, last pushed 9d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,134 once invoked, about $0.0002 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 2026-08-30.
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