aassoiants Thought Tracks
Reads a finished session and produces two things: a model of how the user reasoned this time, and a diff against the last model. The job is to map reasoning method, the moves the user makes to produce thought, not the subject matter they happened to think about.
The whole design rests on one decision: the skill takes no input from the user. No interview, no journaling, no questionnaire. The session trace is the data. The user runs the skill; the skill reads what already happened and emits the artifacts. Asking the user to fill in a form defeats the entire point, so read this again if you are tempted to prompt them for clarification about themselves.
Why this is built the way it is
A passive reader of a conversation will always produce a plausible model and a set of changes that sounds plausible, because language models are fluent at coherence after the fact. That fluency is the central risk: without a guard, this skill becomes a horoscope generator that feels accurate every time and can never be wrong. Two design constraints exist solely to fight that:
Every model commits to falsifiable predictions about how the user will behave in the next session. Vague traits ("thinks in systems") cannot be checked and are forbidden as anchors. Observable moves ("will demote a factual claim to its generating cause before evaluating it") can be checked against the next trace.
The diff separates DRIFT from WOBBLE. A difference between two models is only trusted as real change (DRIFT) if it is backed by the trace, ideally by a prediction the prior model made that this session violated. Differences that are just two summaries phrased differently are WOBBLE and must be quarantined, not reported as the user evolving.
If you drop either constraint, the skill still produces output, it just produces output that cannot be trusted. Don't.
The store
Thought Tracks keeps its models in a single global store at ~/.thought-tracks/ (a folder in the user's home directory, independent of any one agent; on Windows, %USERPROFILE%\.thought-tracks\). One store across all of the user's projects, because the skill models the person, not the project. Alongside the portraits the store keeps a running index.md, one derived row per portrait. Create the directory only on a confirmed first run (see Step 1). The reference files this skill ships with (the output templates and the worked example) live alongside this SKILL.md, in the skill's own references/ folder.
Workflow
Step 1: Locate the prior model
Prior instances live in the store, one file per session. Every file is numbered, NNN-<label>.md, and the number is production order: the order the skill produced the file, stamped as output_at (a UTC timestamp) in the frontmatter. The number, not the session date, is the spine. Live files are NNN-YYYY-MM-DD-<label>.md; the other kinds flag themselves, NNN-backfill-YYYY-MM-DD-<label>.md and NNN-foreign-<label>.md. Order the store by the number (equivalently, by output_at): a new output of any kind appends at the end with the next number. The session dates in the frontmatter are provenance only and may overlap or run earlier than the number, which is expected, a backfill of an old session is produced now. The running index of the whole store is the store's index.md.
model_{n-1} is the most recent prior LIVE model: the extraction: live file with the highest number. Foreign and backfill entries are numbered too but never anchor the live diff (they feed the timeline, not the prediction chain), so skip them when choosing model_{n-1}. Identify that file as model_{n-1}, but do NOT open its body yet: you read its Output A only in Step 3, for the diff. Finding the number and which file is the prior needs the filenames alone, never their contents, so this session's model gets extracted first (Step 2) with the prior portrait still unopened. That ordering is deliberate: it stops this session's pattern count from drifting toward the prior's. Set n to one more than the highest number anywhere in the store, so this run appends at the end. If the user pasted a model into the session, prefer that as model_{n-1} (then it is already in context, so blind extraction is impossible and the count guard in Step 2 has to carry the weight).
Legacy stores. Earlier versions of this skill named files by date, YYYY-MM-DD-<label>.md, with no number. Do not rename them. Treat them as coming before every numbered file, ordered among themselves by their frontmatter dates, and start numbering after them: if no numbered file exists yet, begin at one more than the count of legacy files (a store with 12 legacy portraits begins at 013). The most recent live legacy file serves as model_{n-1} when nothing numbered sits after it.
If the store looks empty, confirm before calling this instance 1. For a brand new user an empty or missing ~/.thought-tracks/ is a genuine first run, but it can also be a path mistake (a wrong home directory, a moved folder), and the two are indistinguishable from the inside. Instance 1 happens exactly once in a store's life, so say what you found and ask the user to confirm this is really their first closeout before treating it as one. On a confirmed first run: create the store, set n = 1, run extraction only, skip the diff, and say the diff begins next session. Never fabricate a baseline to diff against.
Step 2: Extract model_n from the session trace
Read the entire session, attending to reasoning moves, not topics. The topic is scaffolding; the method is the signal.
Write the model in the third person, using the user's name when the trace reveals it, otherwise "the user." It is a portrait that has to stand on its own, readable by a stranger or by a future model asked to reason the way this person does. Plain prose, no em dashes.
The model has a fixed skeleton. Use the exact headings in references/output-templates.md, and see references/worked-example.md for a completed one.
Title (H1). The session label, the surface or project, and the date or date span. Example: # API rate limiter design + rollout · payments service · Mar 3-9, 2026. So every stored file is identifiable at a glance.
Context (one line). Project or repo, the surface (which agent, chatbot, or API), and the kind of work (build, debug, analysis, planning), written so a reader who knows nothing about the project still follows everything below. Infer it from the environment and trace, never ask. It also situates the diff in Step 3.
## Mental model patterns. The core of the model: a run of H3 observations, as many as the session genuinely exercised. Do not cap the count, do not pad it, and do not match the prior portrait's count. The number must arise from this session alone: the prior's count is contamination, not a target, so a leaner session yields fewer patterns and a richer one more (this is why Step 1 leaves the prior unopened until the diff). Discover them fresh from THIS session; an analysis session and a build session surface different patterns, and forcing a prior session's patterns onto this one is the failure to avoid. Each observation is complete in itself: a plain English heading that stands on its own, then four beats in order.
- The heading is a clear claim in ordinary words, understood from the heading alone (
### Changes the frame instead of patching the surface). Not a jargon label like "frame control move." - What it means: unpack the heading into the actual move.
- A concrete example: what they were working on or solving, written for a reader with zero context. Gloss any term specific to the project, ask what a stranger or a future model would need to know, then supply it. Quote real moments.
- Most people do Z: name the default the move deviates from, because a move is only legible as distinctive against a baseline.
- What this means: what the move reveals about how they build or think.
## What it says about {Name}. One short synthesis: what the patterns together say about how they construct reality and what they treat as real. This is the shareable centerpiece, and it may make a larger interpretive claim on one condition: every claim traces back to a pattern named above. This is the climb from "what they do" to "what it means," the step that separates a portrait from an inventory.
## Product building choices. A second spine, parallel to the patterns but pointed at the product they are making instead of the method they are using: the forks where two or more options were genuinely live and they committed to one. A run of ### entries, as many as the session shows and zero if it shows none (a thin session, or one that did no product building, writes nothing here, the same discipline as the pattern count). Discover them blind, before the prior portrait is opened, never forcing a prior session's forks onto this one. Each entry is a plain English heading naming the choice, then three beats in order:
- What it builds: the kind of thing this choice produces in the product (lean, owned, derived, voiced, and so on), written for a reader with zero context, quoting the real moment.
- What they chose against: the option they killed and what it had going for it (faster, easier, more complete, more standard). The rejected branch is the whole point and is usually implicit, so reconstruct it from the trace even when they never stated it aloud.
- What it implies (conjecture): what the choice says about how they think a product should be, phrased as a guess from this session only and marked as such, never as an established trait.
Attribute every fork to their own move (a rename, a cut, an override, a deferral, a reframe that dissolved an option), never the assistant's default that they merely accepted: in many sessions the assistant builds and the user directs, so the taste lives in what they actively chose or refused. Optionally close the section with one line reading the session's product philosophy off the forks together. Keep that line distinct from ## What it says about {Name}: that one is how they reason toward truth, this one is what they treat as a good product. If the product line would only restate the method synthesis, drop it.
## The shadow. The cost or failure mode of the strengths, about the person and not about the model's reliability (that goes under Honest limit). Where does this way of working expose them? A model that only finds good things is broken.
## Predictions. 3 to 5 concrete, observable predictions about the next session, the kind that would embarrass you if wrong. This is the one hard, falsifiable part of the model, and the anchor the next diff bites on. Test each: could the next trace prove this false? If not, rewrite it until it can. When the session surfaced product building choices, promote the strongest fork criterion into a prediction here, a taste bet such as "faced with store versus derive, they derive," so the claim about their product thinking gets checked next session instead of only asserted; mark it as a taste bet so the diff can group it.
## Honest limit. What this read cannot support: a single mode sampled, hype that is mood and not method, too thin a slice to call structure. This is the caveat about the data, kept separate from the shadow.
The interpretive sections (patterns, what it says, the shadow) are the portrait, and they may interpret freely as long as each claim is pinned to a cited moment in the trace. That anchor is the whole defense against a horoscope. The predictions stay the falsifiable spine. Keep the two jobs distinct.
Step 3: Diff model_n against model_{n-1}
Now that model_n is fully extracted, open model_{n-1} (the prior LIVE file you identified in Step 1) and read its Output A. Reading it here, only after extraction, is what keeps the prior from biasing this session's patterns or their count.
The diff has two anchors, and they carry different weight. Keep them separate.
The hard anchor: predictions (this is where the diff actually bites). Take each prediction model_{n-1} made and check it against this session's trace:
- HELD: the predicted move happened. That is stable structure, and naming it is as valuable as naming change.
- DRIFT (trusted): the prior model predicted X and the session did not X. This is the strongest, most trustworthy signal in the whole skill, because it was committed to in advance and the trace overruled it. A clearly different move visible in the trace itself (not just in word choice) also counts, but a violated prediction is the gold standard.
- Untested: the session never created the occasion to confirm or refute the prediction (common when the modes differ). Say so; do not score it either way.
The soft anchor: recurring patterns (a ledger, explicitly weaker). Because the patterns are discovered fresh each session, they will not line up slot for slot, so match them by meaning, not by name. Note which patterns from prior instances reappear here (a pattern recurring across several sessions is mounting soft evidence it is real structure) and which are absent. Treat this as suggestive only: an absent pattern is just as likely an untested mode as a real change, and pattern recurrence is never hard evidence on its own. The predictions police; the pattern ledger only hints. The product building choices feed this same ledger: note which fork criteria recur across sessions, because a criterion that holds across different kinds of product is soft evidence of a stable product taste. A prior taste bet, because it lives among the predictions, is checked exactly like any other prediction in the hard anchor above.
WOBBLE (quarantined, applies to both anchors). A difference with no trace backing (rephrasing, the assistant's vocabulary bleeding into the model, a topic shift dressed up as a method shift, two patterns that are the same move under different names) is noise. List it separately, marked as probable noise. Never report wobble as growth.
Context check, before trusting any drift. Compare the two instances' contexts. If they differ (different project, surface, or kind of work), a method difference is most likely driven by context: the same user in a different mode, not a changed user. Lean toward WOBBLE and overfit, and say so, unless a violated prediction shows the method itself changed within comparable contexts.
Step 4: Emit and store
Output the artifacts in the conversation, Output A then Output B when a prior model exists, using the templates. Then persist this run into the store: write a new number prefixed file NNN-YYYY-MM-DD-<label>.md, where NNN is n zero padded to three digits (the running number from Step 1), the date is this session's date_start, and <label> is the short session label, slugified. The number is the spine the diff follows; the date rides along for reference, and because each number is unique no same day suffix is needed. The other two kinds of entry take the next number the same way but mark their kind in the name and frontmatter: a foreign portrait imported from another chatbot is NNN-foreign-<label>.md (extraction: foreign), and a backfilled past session is NNN-backfill-YYYY-MM-DD-<label>.md (extraction: backfill); both are numbered into the sequence but neither ever anchors the live diff. Never renumber the files already in the store; this run just appends the next number.
Open the file with a YAML frontmatter block carrying output_at (the UTC timestamp when you first write the file, ISO 8601 with a Z, e.g. 2026-06-15T18:30:00Z; this is the permanent ordering key and it never changes), instance (= n), date for a single day session (or date_start and date_end when the session genuinely spanned more than one day), session_label, artifact_type, source, and extraction: live. Every timestamp in the store is UTC. The session dates are provenance only; output_at and the number carry the order. A live closeout is almost always one day, so date is today (UTC); if you want the exact start, it is the first timestamped line of the current transcript. Put both Output A and (when there was a prior) Output B in the file.
Never overwrite an existing file. Each session is its own file, so the full trajectory stays recoverable and Step 1 can always find the most recent prior model by its number. If you ever re-run an existing portrait in place, leave its output_at and its number alone and set updated_at (UTC) instead: first produced is output_at, last touched is updated_at.
Finally, update the index. Append one row for the new file to the store's index.md, in the table's column order: | NNN | <output_at> | <updated_at> | <instance> | <session start> | <session end> | live | <filename without .md> | <note> | (for a fresh output, updated_at and note are blank). If the index does not exist yet, create it with the header row | # | output_at (UTC) | updated_at | inst | session start | session end | src | name | note |. The index is a derived view of the portraits' frontmatter, ordered by output_at, so a new output appends at the bottom; fix any classification in the portrait itself, never by hand in the table.
Honesty rules
- Early instances are mostly WOBBLE. One or two prior sessions make a terrible baseline, and the assistant's own phrasing contaminates the model. Say this honestly when it is true, "not enough trace yet to separate signal from noise" is the correct output for instance 2, not a confident trend. Faking early confidence is the failure mode.
- Bias toward the performed self. A passive reader only sees sessions where the user showed up. The model will overfit to whatever mode they were in. When a later session violates that, read it as "model was overfit," not "user changed", and say so.
- Don't flatter. This maps reasoning; it is not a compliment generator. Note sloppy moves, dropped threads, and unexamined priors as readily as strengths. A model that only finds good things is broken.
- Infer worldview, not interiors. Reading how the user constructs reality from the moves they actually made is the job, and it is what makes the model worth sharing. But never invent motives, history, diagnoses, or feelings the trace does not show. The test is one line: every interpretive claim, including the synthesis under "What it says about {Name}", must point back to a cited moment. Inference from evidence is the work; projection past it is the failure.
Output
Two artifacts, in order, using references/output-templates.md:
- Output A:
model_n, the portrait: anH1title, a one line Context,## Mental model patterns(theH3observations),## What it says about {Name},## Product building choices(theH3forks, when the session had them),## The shadow,## Predictions,## Honest limit. - Output B: the diff (held + drift on the predictions, taste bets included; the soft ledger of recurring patterns and fork criteria; quarantined wobble; explanation)
For instance 1, emit Output A only and state that the diff begins next session.
See references/worked-example.md for a completed pair, so the shape is concrete before you write.