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reconcile

Cross-checks invoices, payments, and deposits and flags mismatches.
description: "Triggers on prompt mention of 'reconcile'."
personal 2 files 10 recent evals

What it does for you

Cross-checks invoices, payments, and deposits and flags mismatches.

What it produces

A recent result, so you can see the kind of work it returns.

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How to get it

These run inside the Snappy workspace. Want this working in your business? I set skills like this up with you, in one focused week.

Work with me
For developers how this skill is built, graded, and how it runs

at a glance- the short version

eval modeauto
categoryOps
stages2
dependsxano, freshbooks

what's inside - the parts that make up a skill 2/4 present

A skill is just a few plain-text files. Only the main one is required. The rest are optional, added as the work needs them. This is what the skill is made of; how it runs is just below.

The skill
state/skills/reconcile/SKILL.md present
the skill itself, in plain text
The main file. It says what the skill is and lays out the steps in plain English.
Code
state/lib/reconcile.ts not present
code the skill can run
Optional. Many skills are just words and need no code at all.
Scripts
state/bin/reconcile/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/reconcile/AGENTS.md present
what the AI loads on the fly
Loaded automatically the moment this skill is needed. Kept short on purpose.

how it's graded - what counts as a good run 5 criteria · 3 deterministic · 2 judge

Each row is one thing a good run has to get right. deterministic means a quick check decides, pass or fail. judge means the AI reads the result and rates it. Grading each piece on its own (instead of one overall score) shows exactly where a run fell short, so the fix is obvious.

name
kind
check
output_file_exists
deterministic
The file 'state/log/reconcile/<month>.md' must exist after skill execution, where <month> is the input month.
all_sources_loaded
deterministic
The skill evaluation must report 'all_three_sources_loaded' as true, or 'primary_issue' is not 'source-missing'.
correct_output_shape
deterministic
The skill evaluation must report 'shape_ok' as true.
unmatched_reporting
judge
The content of 'state/log/reconcile/<month>.md' must clearly list and itemize any 'unmatched_invoices', 'unmatched_payments', and 'unmatched_deposits' flagged by the skill, providing context like amount, date, and client/memo.
amount_comparison_correctness
judge
The reconciliation log, if reviewing specific matches, must demonstrate that amount comparisons were performed in cents and not dollars, avoiding floating-point issues.

how it runs - the shared frame every skill uses 3/5 present

Every skill runs the same way. One part does the work, a separate part checks it, and a short loader hands the AI exactly what it needs for the job. Anything this skill doesn't use shows a one-line note saying why, on purpose, not by accident.

makes the work The worker
not present

No work step here. This is probably a skill that reads or coordinates, not one that produces something.

checks the work The reviewer
inferred
shape gate (matched/unmatched from the check command
The check is a quick command that confirms the result looks right.
frame
learns Self-correction
present
fixes itself learns from gaps
When a run hits a gap, the skill gets edited on the spot [FIXED] or queued for a bigger rewrite [LOGGED], so it keeps getting better.
tidies up Background fixes
present
queued for rewrite runs in the background
Bigger fixes that can't be made on the spot get queued and rewritten in the background later.
remembers Run history
present
state/log/evals.ndjson unknown runs
Every run is written down here, so the next time this skill is used it already knows how the last runs went.
Critical rules the things this skill must not get wrong
  1. ALWAYS compare amounts in CENTS. Never in dollars. Float comparison on dollars guarantees false negatives.
  2. ALWAYS complete Stripe API pagination. Partial fetch = false-negative unmatched payments. Do not bail early.
  3. ALWAYS treat unmatched as the SIGNAL, not failure. unmatched_invoices/payments/deposits > 0 does NOT score 0.0 — that's the entire reason this skill exists.
  4. Score 0.0 only on shape failure or missing source. Score 0.5 if bank CSV missing (primary_issue: "source-missing"); the manual CSV drop in sources/finance/ is human-owned and may not be present.
  5. Match window: ±3 days, exact amount + memo overlap. Two-pass: pass 1 exact week, pass 2 memo overlap.

what it has learned - fixes written back in over time sample

When a run hits something this skill didn't handle, the fix gets written back into the skill so it doesn't happen again. FIXED means it was corrected on the spot. LOGGED means it's queued for a bigger rewrite. Either way, the skill gets a little better and never makes the same mistake twice.

  1. Loading feedback rows…

how the work flows- step by step

inputs xanofreshbooks
1 auditor
verify
shape gate (matched/unmatched arrays present), all three sources loaded
2 data
eval log
`state/log/evals.ndjson` (skill: "reconcile")

SKILL.md- the skill, written out in plain English

Backed by: state/lib/xano.ts (Stripe side is an external-dep phantom - manual cross-check until a Stripe lib lands)

reconcile

Three-way reconciliation. Reads:

  • Xano invoices table for $month
  • Stripe charges via API for $month
  • Bank deposit entries from the manual CSV drop in sources/finance/

Matches by amount + date window ±3 days + client memo. Flags unmatched.

Steps

  1. Fetch all three sources.
  2. Normalize to {amount_cents, date, client, source_ref}.
  3. Match pass 1: exact amount + same week.
  4. Match pass 2: exact amount + memo overlap.
  5. Emit {matched, unmatched_invoices, unmatched_payments, unmatched_deposits}.
  6. Write state/log/reconcile/<month>.md.

Eval

score("reconcile", run_id, {
  score:
    shape_ok && all_three_sources_loaded ? 1.0 :
    shape_ok ? 0.5 :
    0.0,
  matched_count: matched.length,
  unmatched_invoices: u_i.length,
  unmatched_payments: u_p.length,
  unmatched_deposits: u_d.length,
  primary_issue:
    !all_three_sources_loaded ? "source-missing" :
    u_i.length + u_p.length + u_d.length > 0 ? "unmatched-present" :
    null,
});

Unmatched ≠ score 0 - unmatched is the signal the skill exists to surface. Score 0 only on shape failure or missing sources.

Gotchas

  • Stripe API pagination must complete; partial fetch = false negatives.
  • Bank CSV is manual - if missing, score 0.5 with `primary_issue:

"source-missing"`.

  • Amount comparison in cents. Never in dollars.

Rubric

criteria:
  - name: output_file_exists
    kind: deterministic
    check: "The file 'state/log/reconcile/<month>.md' must exist after skill execution, where <month> is the input month."
  - name: all_sources_loaded
    kind: deterministic
    check: "The skill evaluation must report 'all_three_sources_loaded' as true, or 'primary_issue' is not 'source-missing'."
  - name: correct_output_shape
    kind: deterministic
    check: "The skill evaluation must report 'shape_ok' as true."
  - name: unmatched_reporting
    kind: judge
    check: "The content of 'state/log/reconcile/<month>.md' must clearly list and itemize any 'unmatched_invoices', 'unmatched_payments', and 'unmatched_deposits' flagged by the skill, providing context like amount, date, and client/memo."
  - name: amount_comparison_correctness
    kind: judge
    check: "The reconciliation log, if reviewing specific matches, must demonstrate that amount comparisons were performed in cents and not dollars, avoiding floating-point issues."

AGENTS.md- what the AI loads when this skill comes up

reconcile - loader

Per-turn rules for the reconcile skill. Full reference: state/skills/reconcile/SKILL.md. Do not skip these.

Critical Rules

  • ALWAYS compare amounts in CENTS. Never in dollars. Float comparison on dollars guarantees false negatives.
  • ALWAYS complete Stripe API pagination. Partial fetch = false-negative unmatched payments. Do not bail early.
  • ALWAYS treat unmatched as the SIGNAL, not failure. unmatched_invoices/payments/deposits > 0 does NOT score 0.0 - that's the entire reason this skill exists.
  • Score 0.0 only on shape failure or missing source. Score 0.5 if bank CSV missing (primary_issue: "source-missing"); the manual CSV drop in sources/finance/ is human-owned and may not be present.
  • Match window: ±3 days, exact amount + memo overlap. Two-pass: pass 1 exact week, pass 2 memo overlap.

Commands

| ui dashboard | state/skills/reconcile/resources/ui.openui | |invoke (TS): chain state/lib/xano.ts (invoices) + Stripe API + sources/finance/<month>.csv |verify: shape gate (matched/unmatched arrays present), all three sources loaded |eval log: state/log/evals.ndjson (skill: "reconcile") |output: state/log/reconcile/<month>.md

OpenUI Resource

  • Skill-owned OpenUI Lang resource: state/skills/reconcile/resources/ui.openui. Read it before rendering or editing this skill's generated component surface.
  • Treat this resource as a first-class artifact of the skill, not a generic chat response. Improve it when the skill's user-facing output needs to become richer.
  • System resources compose OpenUI primitives and inherit SnappyChat tokens. Use ui_contract: branded in SKILL.md only for deliberate platform or client visuals.

Known Pitfalls

  • Stripe lib is a phantom (per skill .md "external-dep phantom - manual cross-check until a Stripe lib lands"). Until then, manual cross-check is acceptable but score 0.5.
  • The requires: [month] payload key is mandatory. Format YYYY-MM.
  • "Unmatched" is per-source: an invoice with no payment, a payment with no invoice, a deposit with no payment. Three independent buckets.

Self-Test

An agent reading this should correctly:

  1. [ ] Compare amounts in cents, never dollars
  2. [ ] Score 1.0 on a clean run with unmatched > 0 (the signal, not failure)
  3. [ ] Score 0.5 (not 0.0) when bank CSV is missing

Self-report

If this loader fell short, append a line:

echo "[$(date -u +%FT%TZ)] reconcile: <what was missing>" >> state/log/loader-feedback.log

<!-- kernel-ok: historical attribution for the PID-loader pattern. -->

Self-correcting loader (PID feedback)

This is the rule that makes loaders learn. Ported from snappy-kernel, where it demonstrably worked: the snappy-dom-cartographer skill mapped Skool admin UI once, every later ship-agent inherited the full map for free.

Before you finish, do two things:

1. Fix gaps (P - proportional). MANDATORY. If this loader didn't cover your case - if you had to read another file, run an undocumented command, work around a wrong selector, or discover a quirk - you MUST attempt an Edit to this AGENTS.md before you log.

  • Default action: edit this file. Surgical. Add the missing line, table

row, or code example where it belongs. No restructuring.

  • Only LOGGED is allowed when: the fix needs >10 lines, spans multiple

files, or requires a structural rewrite. In that case the state/regen/drain.sh queue picks it up asynchronously.

  • "I didn't have time" / "it's minor" / "the next agent will figure it out"

are NOT valid reasons. Minor gaps compound - that's the whole point of this loop.

  • Concrete example of a valid P-fix: discover agent-browser screenshot

takes path positionally not as --output <path>. One line in a table. Edit it in. Do not just log it.

  • The goal: the next agent never has to leave the loader.

2. Log the result.

echo "[$(date -u +%FT%TZ)] reconcile: <what was missing or fixed> [FIXED|LOGGED]" >> state/log/loader-feedback.log
  • FIXED = you patched this loader inline (P-fix).
  • LOGGED = too large for inline; the PostToolUse enqueue + Stop-hook drain

will rewrite the loader from scratch on next session-end.

Do not skip this. Every agent run must leave the system better than it found it. The loader is the setpoint; you are the sensor; the gap is the error signal; closing the gap is the correction.

api.ts- the code it can call

⚠ no api.ts - this skill has no typed action surface

scripts- helper scripts it can run

prose-only skill - 2 inline code blocks live in SKILL.md above (no state/bin/ sidecar yet).

how we check it- the checks, plus the last 10 runs

rubric auto no rubric declared
recent mean 1.00 · 10 runs actor/auditor: unverifiable
deps xano freshbooks
timestamp verb score primary_issue artifact
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:58Z - 1.00 - -
2026-04-21 15:57Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:58Z - 1.00 - -
2026-04-21 15:57Z - 1.00 - -
2026-04-21 03:53Z - 1.00 - -
2026-04-25 04:11Z - 1.00 - -
2026-04-21 15:58Z - 1.00 - -