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 skills add IamK77/Skill --skill reckoninggit clone --depth 1 https://github.com/IamK77/SkillWrote 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/iamk77/skill/reckoning)<a href="https://agentmods.dev/skills/iamk77/skill/reckoning"><img src="https://agentmods.dev/badge/skills/iamk77/skill/reckoning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/iamk77/skill/reckoning"><img src="https://agentmods.dev/badge/skills/iamk77/skill/reckoning.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00032 | $0.00978 |
| Opus 5.5 | $0.00013 | $0.00391 |
| Sonnet 5.5 | $0.00006 | $0.00196 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
reckoning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reckoning
Analyse research results against the recorded plan, inspect failure patterns, and bound the claims. Use for evidence review before writing or making a research decision.
Inputs
Experimental plan; raw results and run manifest; deviations; analysis code; intended claim.
Run record
Use checklist 0.5 or newer for this SOP. Reading or reloading the skill must not reset work. For a new task, run checklist init "${CLAUDE_SKILL_DIR}" --new --path "/absolute/project"; retain its returned ID as RUN. For existing work, use checklist resume "$RUN", then checklist show --run "$RUN". Outside Claude, replace the skill-directory variable with this directory’s actual path.
Commands below record work after it is done. Replace evidence placeholders with concise artifact locations and observations. Manual confirmations are not independent proof; a sensor pass covers only the command and inputs it observed. verify does not close a stage; advance checks that its required readings are fulfilled. Use report --run "$RUN" for the retained history.
Procedure
audit
Check that runs, exclusions, and metric definitions match the plan. Identify missing or failed runs and data leakage concerns before estimating an effect.
Record these artifacts after doing the work:
- Included, excluded, failed, and missing runs are accounted for.
checklist check audit population --run "$RUN" --evidence "<artifact and observation>" - Metric definitions and deviations are checked against the plan.
checklist check audit protocol --run "$RUN" --evidence "<artifact and observation>"
When this stage is fulfilled: checklist advance audit --run "$RUN".
analyse
Inspect distributions and meaningful subgroups, estimate uncertainty appropriately, and test plausible alternative explanations. Distinguish exploratory discoveries from planned comparisons.
What ships with it
7 files 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.
- 1mo ago Changed · -91 lines · -139 tokens per session f254ddb762eb
- 1mo ago First seen · 180 lines · 171 tokens per session scan A 008cc0acdc57
reckoning is a skill published in the GitHub repository IamK77/Skill (2 stars, last pushed 15d ago), licensed Apache-2.0. It adds 32 tokens to every session and 978 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
health-nutrition-expert
Apply cutting-edge 2025 nutrition science on longevity, metabolic health, gut microbiome, and evidence-based dietary patterns for optimal vitality and disease prevention. Use when planning a diet, evaluating a nutrition claim, or applying evidence-based guidance on longevity, metabolic, and gut health.
ai-analyzer
An AI health-analysis system that combines health, lifestyle, mental-health, and medical-history data. It detects unusual patterns, estimates selected health risks, answers questions about the data, and creates reports.
feature-engineering
Use when building or improving time series forecasting models and the user asks about exogenous variables, calendar features, rolling statistics, cyclical encoding, differencing, or feature scaling — or when forecast accuracy has plateaued and new features may help.
vast-experiment-ops
Use when running or reporting ML experiments on rented Vast.ai GPU instances — diagnosing strict FP32 equivalence checks that pass on CPU but fail on CUDA (cuDNN TF32), parsing JSON metrics collected over SSH, and keeping held-out/test-scope reporting accurate.
tabpfn-regress
Run a TabPFN regression baseline, generate the first submission, then optimize with GBT ensembles and regression-specific post-processing (clipping, target transforms, rank blending). Use after tabpfn-explore has prepared the data and CV folds.
tabpfn-explore
EDA, data profiling, adversarial validation, preprocessing checks, CV scheme setup, and API budget verification for tabular Kaggle competitions. Run at the start of every new competition before any modeling.