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 naimkatiman/continuous-improvement --skill state-reconciliationgit clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/state-reconciliation)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/state-reconciliation"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/state-reconciliation/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/naimkatiman/continuous-improvement/state-reconciliation"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/state-reconciliation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.00989 |
| Opus 5 | $0.00036 | $0.00495 |
| Sonnet 5 | $0.00014 | $0.00198 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
state-reconciliation 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 8d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
State Reconciliation Skill
A pre-dispatch invariant. Before the verification ladder runs (or any auto-loop unit dispatches), reconcile the three state sources — DB / disk / in-memory — and surface any divergence as a blocker, not a silent re-dispatch.
When to Use
- Before Phase 1 of
verification-loopif the project tracks runtime state outside source control (sqlite, KV, redis,completed-units.json, milestone JSON, lockfiles). - Before any auto-loop iteration in long-running sessions.
- After a crash, pause/resume, or context compaction — the in-memory loop state is gone but DB and disk persist.
- When a flag like
is_sketch,in_progress, or a stale lock keeps re-firing the same unit.
Why This Skill Exists
GSD-2's CONTEXT.md names "State Reconciliation" as one of four runtime invariant modules. Common failure modes it catches:
- Stale flags re-dispatch completed units —
is_sketch, stale worker rows, stale sequence/dependency rows that no one cleared. - Disk artifacts present, DB status lags —
PROJECT.mdmilestone registration, completion timestamps, roadmap divergence. - Recovery helpers exist but aren't wired into dispatch — divergence accrues silently between iterations.
A verification ladder that runs against unreconciled state reports green on the wrong inputs.
The Reconciliation Contract
For every state class the project tracks, define and run before dispatch:
- Authority — which surface is the source of truth (DB, disk artifact, in-memory loop state).
- Projection — direction of state flow (DB → disk, disk → DB, or both with a named winner).
- Reconciliation — the deterministic check that proves authority and projection match.
- Failure mode — what happens on divergence (block dispatch, repair, ask operator).
Output a single fenced block before any dispatch, so the operator sees the resolved state before it costs a misread:
state-reconciliation (resolved):
participants table: DB authoritative; disk projection at data/participants.json — match (1247 rows)
completed_units.json: project root authoritative after crash; worktree projection — match
is_sketch flag: DB authoritative — clear (no re-dispatch risk)
open lock at /tmp/foo.lck: stale (PID 1234 not running) — STALE, blocks dispatch
milestone registration: PROJECT.md authoritative; DB row — DIVERGED (DB shows in_progress, disk shows complete)
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.
- 8d ago First seen · 65 lines · 71 tokens per session scan A e3430d5ed3f6
state-reconciliation is a skill published in the GitHub repository naimkatiman/continuous-improvement (7 stars, last pushed 2d ago), licensed MIT. It adds 71 tokens to every session and 989 once invoked, about $0.0004 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 skills, from other repositories
ml-debug
Use when something is failing in ML/AI work — OOM, NaN, divergence, crashes, bad throughput, wrong outputs, dependency conflicts.
agent-pitbook
Search and contribute to Agent Pitbook, a structured debugging pit knowledge base for coding agents. Use when Codex is diagnosing recurring local-development failures, sandbox issues, dependency installation errors, Docker or port problems, MCP/tool quirks, browser automation failures, or when a verified debugging…
sglang-diffusion-benchmark-profile
Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
binary-triage
Performs initial binary triage by surveying memory layout, strings, imports/exports, and functions to quickly understand what a binary does and identify suspicious behavior. Use when first examining a binary, when user asks to triage/survey/analyze a program, or wants an overview before deeper reverse engineering.
quota-debug
Diagnose Kubernetes native ResourceQuota and LimitRange admission rejections (exceeded quota, forbidden by LimitRange, FailedCreate). Checks namespace quotas, current usage, LimitRange constraints, and ReplicaSet events to identify why pods cannot be created. Not applicable to Volcano Queue — use…